{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Sensitivity analysis on a NetLogo model with SALib\n",
    "\n",
    "This notebook provides a more advanced example of interaction between NetLogo and a Python environment, using the SALib library (Herman & Usher, 2017; available through the pip package manager) to sample and analyze a suitable experimental design for a Sobol global sensitivity analysis. All files used in the example are available from the pyNetLogo repository at https://github.com/quaquel/pyNetLogo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ensuring compliance of code with both python2 and python3\n",
    "\n",
    "from __future__ import division, print_function\n",
    "try:\n",
    "    from itertools import izip as zip\n",
    "except ImportError: # will be 3.x series\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import pyNetLogo\n",
    "\n",
    "#Import the sampling and analysis modules for a Sobol variance-based sensitivity analysis\n",
    "from SALib.sample import saltelli\n",
    "from SALib.analyze import sobol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SALib relies on a problem definition dictionary which contains the number of input parameters to sample, their names (which should here correspond to a NetLogo global variable), and the sampling bounds. Documentation for SALib can be found at https://salib.readthedocs.io/en/latest/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "problem = { \n",
    "  'num_vars': 6,\n",
    "  'names': ['random-seed',\n",
    "            'grass-regrowth-time',\n",
    "            'sheep-gain-from-food',\n",
    "            'wolf-gain-from-food',\n",
    "            'sheep-reproduce',\n",
    "            'wolf-reproduce'], \n",
    "  'bounds': [[1, 100000],\n",
    "             [20., 40.], \n",
    "             [2., 8.], \n",
    "             [16., 32.],\n",
    "             [2., 8.],\n",
    "             [2., 8.]]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by instantiating the wolf-sheep predation example model, specifying the _gui=False_ flag to run in headless mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "netlogo = pyNetLogo.NetLogoLink(gui=False)\n",
    "model_file = os.path.join(netlogo.netlogo_home, 'models/Sample Models/Biology/Wolf Sheep Predation.nlogo')\n",
    "netlogo.load_model(model_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SALib sampler will automatically generate an appropriate number of samples for Sobol analysis. To calculate first-order, second-order and total sensitivity indices, this gives a sample size of _n*(2p+2)_, where _p_ is the number of input parameters, and _n_ is a baseline sample size which should be large enough to stabilize the estimation of the indices. For this example, we use _n_ = 1000, for a total of 14000 experiments.\n",
    "\n",
    "For more complex analyses, parallelizing the experiments can significantly improve performance. An additional notebook in the pyNetLogo repository demonstrates the use of the ipyparallel library; parallel processing for NetLogo models is also supported by the Exploratory Modeling Workbench (Kwakkel, 2017)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "n = 10\n",
    "param_values = saltelli.sample(problem, n, calc_second_order=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampler generates an input array of shape (_n*(2p+2)_, _p_) with rows for each experiment and columns for each input parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_values.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assuming we are interested in the mean number of sheep and wolf agents over a timeframe of 100 ticks, we first create an empty dataframe to store the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame(columns=['Avg. sheep', 'Avg. wolves'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then simulate the model over the 14000 experiments, reading input parameters from the param_values array generated by SALib. The repeat_report command is used to track the outcomes of interest over time. \n",
    "\n",
    "To later compare performance with the ipyparallel implementation of the analysis, we also keep track of the elapsed runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "t0=time.time()\n",
    "\n",
    "for run in range(param_values.shape[0]):\n",
    "    \n",
    "    #Set the input parameters\n",
    "    for i, name in enumerate(problem['names']):\n",
    "        if name == 'random-seed':\n",
    "            #The NetLogo random seed requires a different syntax\n",
    "            netlogo.command('random-seed {}'.format(param_values[run,i]))\n",
    "        else:\n",
    "            #Otherwise, assume the input parameters are global variables\n",
    "            netlogo.command('set {0} {1}'.format(name, param_values[run,i]))\n",
    "            \n",
    "    netlogo.command('setup')\n",
    "    #Run for 100 ticks and return the number of sheep and wolf agents at each time step\n",
    "    counts = netlogo.repeat_report(['count sheep','count wolves'], 100, include_t0=False)\n",
    "    \n",
    "    #For each run, save the mean value of the agent counts over time\n",
    "    results.loc[run, 'Avg. sheep'] = counts['count sheep'].values.mean()\n",
    "    results.loc[run, 'Avg. wolves'] = counts['count wolves'].values.mean()\n",
    "    \n",
    "elapsed=time.time()-t0 #Elapsed runtime in seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.821950912475586"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elapsed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"to_csv\" dataframe method provides a simple way of saving the results to disk.\n",
    "\n",
    "Pandas supports several more advanced storage options, such as serialization with msgpack, or hierarchical HDF5 storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('Sobol_sequential.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "results = pd.read_csv('Sobol_sequential.csv', header=0, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. sheep</th>\n",
       "      <th>Avg. wolves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>133.57</td>\n",
       "      <td>112.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>137.11</td>\n",
       "      <td>130.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>133.57</td>\n",
       "      <td>112.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>133.57</td>\n",
       "      <td>112.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>296.50</td>\n",
       "      <td>95.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg. sheep  Avg. wolves\n",
       "0      133.57       112.01\n",
       "1      137.11       130.65\n",
       "2      133.57       112.01\n",
       "3      133.57       112.01\n",
       "4      296.50        95.42"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then proceed with the analysis, first using a histogram to visualize output distributions for each outcome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "sns.set_context('talk')\n",
    "fig, ax = plt.subplots(1,len(results.columns), sharey=True)\n",
    "\n",
    "for i, n in enumerate(results.columns):\n",
    "    ax[i].hist(results[n], 20)\n",
    "    ax[i].set_xlabel(n)\n",
    "\n",
    "ax[0].set_ylabel('Counts')\n",
    "\n",
    "fig.set_size_inches(10,4)\n",
    "fig.subplots_adjust(wspace=0.1)\n",
    "#plt.savefig('JASSS figures/SA - Output distribution.pdf', bbox_inches='tight')\n",
    "#plt.savefig('JASSS figures/SA - Output distribution.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bivariate scatter plots can be useful to visualize relationships between each input parameter and the outputs. Taking the outcome for the average sheep count as an example, we obtain the following, using the scipy library to calculate the Pearson correlation coefficient (r) for each parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib\n",
    "import scipy\n",
    "\n",
    "nrow=2\n",
    "ncol=3\n",
    "fig, ax = plt.subplots(nrow, ncol, sharey=True)\n",
    "sns.set_context('talk')\n",
    "y = results['Avg. sheep']\n",
    "\n",
    "for i, a in enumerate(ax.flatten()):\n",
    "    x = param_values[:,i]\n",
    "    sns.regplot(x, y, ax=a, ci=None, color='k',scatter_kws={'alpha':0.2, 's':4, 'color':'gray'})\n",
    "    pearson = scipy.stats.pearsonr(x, y)\n",
    "    a.annotate(\"r: {:6.3f}\".format(pearson[0]), xy=(0.15, 0.85), xycoords='axes fraction',fontsize=13)\n",
    "    if divmod(i,ncol)[1]>0:\n",
    "        a.get_yaxis().set_visible(False)\n",
    "    a.set_xlabel(problem['names'][i])\n",
    "    a.set_ylim([0,1.1*np.max(y)])\n",
    "\n",
    "fig.set_size_inches(9,9,forward=True) \n",
    "fig.subplots_adjust(wspace=0.2, hspace=0.3)\n",
    "#plt.savefig('JASSS figures/SA - Scatter.pdf', bbox_inches='tight')\n",
    "#plt.savefig('JASSS figures/SA - Scatter.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates a positive relationship between the \"sheep-gain-from-food\" parameter and the mean sheep count, and negative relationships for the \"wolf-gain-from-food\" and \"wolf-reproduce\" parameters.\n",
    "\n",
    "We can then use SALib to calculate first-order (S1), second-order (S2) and total (ST) Sobol indices, to estimate each input's contribution to output variance. By default, 95% confidence intervals are estimated for each index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "Si = sobol.analyze(problem, results['Avg. sheep'].values, calc_second_order=True, print_to_console=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a simple example, we first select and visualize the first-order and total indices for each input, converting the dictionary returned by SALib to a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Si_filter = {k:Si[k] for k in ['ST','ST_conf','S1','S1_conf']}\n",
    "Si_df = pd.DataFrame(Si_filter, index=problem['names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ST</th>\n",
       "      <th>ST_conf</th>\n",
       "      <th>S1</th>\n",
       "      <th>S1_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>random-seed</td>\n",
       "      <td>0.090374</td>\n",
       "      <td>0.081646</td>\n",
       "      <td>0.039201</td>\n",
       "      <td>0.156802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>grass-regrowth-time</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sheep-gain-from-food</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wolf-gain-from-food</td>\n",
       "      <td>0.011826</td>\n",
       "      <td>0.336804</td>\n",
       "      <td>-0.010866</td>\n",
       "      <td>0.381865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sheep-reproduce</td>\n",
       "      <td>0.889222</td>\n",
       "      <td>0.853433</td>\n",
       "      <td>0.168297</td>\n",
       "      <td>0.403358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wolf-reproduce</td>\n",
       "      <td>0.878107</td>\n",
       "      <td>0.828489</td>\n",
       "      <td>0.171208</td>\n",
       "      <td>0.572089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            ST   ST_conf        S1   S1_conf\n",
       "random-seed           0.090374  0.081646  0.039201  0.156802\n",
       "grass-regrowth-time   0.000000  0.000000  0.000000  0.000000\n",
       "sheep-gain-from-food  0.000000  0.000000  0.000000  0.000000\n",
       "wolf-gain-from-food   0.011826  0.336804 -0.010866  0.381865\n",
       "sheep-reproduce       0.889222  0.853433  0.168297  0.403358\n",
       "wolf-reproduce        0.878107  0.828489  0.171208  0.572089"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Si_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "fig, ax = plt.subplots(1)\n",
    "\n",
    "indices = Si_df[['S1','ST']]\n",
    "err = Si_df[['S1_conf','ST_conf']]\n",
    "\n",
    "indices.plot.bar(yerr=err.values.T,ax=ax)\n",
    "fig.set_size_inches(8,4)\n",
    "\n",
    "#plt.savefig('JASSS figures/SA - Indices.pdf', bbox_inches='tight')\n",
    "#plt.savefig('JASSS figures/SA - Indices.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"sheep-gain-from-food\" parameter has the highest ST index, indicating that it contributes over 50% of output variance when accounting for interactions with other parameters. However, it can be noted that the confidence bounds are overly broad due to the small _n_ value used for sampling, so that a larger sample would be required for reliable results. For instance, the S1 index is estimated to be larger than ST for the \"random-seed\" parameter, which is an artifact of the small sample size.\n",
    "\n",
    "We can use a more sophisticated visualization to include the second-order interactions between inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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q1IGHhweMjY3x5MkTBAQE4P79+/j1118/eN9Q+SSRSDBq1Cgxma+npwd3d3fx3AkMDERERAS2bNmCWrVqFVrff//7X7mEl1TXrl3F5VWrVmHr1q0AAC0tLbRv3x7NmzeHkZERUlJSEBoaiuvXr0MQBCQkJOD777/HqVOn5B4iyMrKwtixY8WEV7169cTzOjk5GQ8ePMDVq1cBAGfPnsWCBQuwePFiuZj8/Pywbt068XN37NgRjRo1gp6eHmJiYnD27FkkJibi/fv3mDRpEk6cOIE6dep8wN5V7vr16woJxoL7qDjv4dOmTRMTZ1paWnB2dkbLli2RlZWFS5cu4eHDhzhy5IhCz19VDh8+XGj84eHhGD58uPg7xsjICB4eHrC0tERaWhqCg4Nx//595OTkYNOmTYiLi5P7neHq6gofHx8A+UmvCRMmKGzv/v37SElJEf9+8OAB0tLSFK6hubm5CA4OBgA0bNgQFhYW6NChAwwNDVVey5s3b670s48cORJpaWkwMTFBly5dULt2bbx9+xanT5/GmzdvkJGRgTlz5qBJkyaoX79+4TuzgDp16mDmzJkq78uNGjX64Dp37NiB27dvQ0tLC05OTmjRogVSUlJw/vx5REdHIzc3F4sWLUJmZiYWLlwIAHB2doatrS2ys7Nx+vRp8Txbu3YtOnbsiCZNmihsZ9GiRXI9ne3t7WFnZ4cKFSogKioKgYGBSEpKwp07d+Dp6YlDhw4V6bcLERERERGRRgQiKhY3btwQrKysBCsrK2H48OFCZmamQpnQ0FDB1tZWLHf79m3xvUOHDomvW1lZCW3atBEuX76sUMfRo0fFMo0bNxbi4uIUyixYsEAsM3jwYOH169cKZV68eCF0795dLLdlyxaFMnfu3BGaNm0qWFlZCba2tsKZM2cUymRmZgoLFy4U6+nZs6eQk5MjV6bgZ2vSpImwf/9+hbr+/PNPwdraWrCyshJsbGyEZ8+eKZTRVG5urtCnTx9xm1OnThXS09Plyrx+/VqujJWVldCxY0eFutasWSO+P2DAACEpKUmhTE5OjrB48WKxXOvWrYWsrCy5MteuXRPfnzVrlkIdsnFYWVkJmzdvFnJzc+XKREVFCR07dhTLbNy4sSi7R/Ty5Uu541aZ4cOHy8Xl6ekp3Lt3T0hPTxdCQ0OFY8eOiWV9fX3Fcs2bNxeOHz+uUF98fLxcnV26dFE4XwoeM87OzkJ4eLhcmdjYWKF9+/Zy5ZycnBTKJScnC7169RLL/Pnnn0XcW4qxrV27VmmZadOmiWXc3NyEBw8eKJQJCwsTnJ2dxXIzZsxQKHPp0iW58/3PP/9UOCZSUlKE77//Xizn4OAgvHnzRq5MZmam3HEzf/58ITs7W67M8+fPhU6dOsntT2XHKX0+tm/fLneOPXz4UO59iUQirFixQuHadO3aNbFMwXO1VatWwtGjR4WUlBTh1atXwrZt28Rj7fnz5+I9xcbGRrh69arSuG7cuCF3rzxx4oTc+0eOHBHfmzZtmsI5IQiCEBAQIG6rSZMmQmxsrNz70mPd1tZWCAsLU1g/LS1N8PLyErfzyy+/aLZTlZC9zkr/+fj4CG/fvhUSExOFo0ePCk+ePBHLF9c93N/fX3zfzs5OCA4OViizc+dO8b4r/Xfo0CG5MrL3LisrK6Fp06bCX3/9JSQlJQnx8fHC7t27xWtOVlaW3LVm5MiRwtu3bxW2e/jwYcHGxkYsV/A3gYuLi2BlZSU0a9ZMyMjIUFj/v//9r8I+vXjxokI52d9my5cvl3tPk2u57GexsrISfvjhByEtLU2uTHJystC/f3+xzLx585TWpSlN7str167V+Puys7OTO2elMffo0UPh3C14jGRlZQmjRo0SyyxatEghFtnjrEOHDsKNGzcUyiQnJwuTJ08Wy3l7e3/obiEiIiIiItJY4RMCEZFG7t69Ky5/8803SodSatGiBUaNGgUgv+dHaGioyvqWLl0qPnUsq3fv3uIcQHl5eQpDKcbExMDX1xdAfs+mbdu2yfVAkbK0tMTmzZvFOLdt24aMjAy5MmvXrkVOTo4YT+fOnRXqMTAwwM8//yz2LHn8+DFOnz6t8nMB+UMjDRo0SOH1kSNHivNQSCQShTk3PsTx48fx8OFDAPlPba9YsUJhuMGaNWti+/btKoeZlJLttbR06VKYmJgolNHR0cGPP/4ovpeSkoKXL18WOf5+/fop7YFmYWGB77//Xvz72rVrRd5GUVSpUgXbtm2DjY0NKlasiBYtWqBXr14A8odBku2RtGDBAvTs2VOhDnNzc2zZskWc+yQiIkJh6K2ClixZojCnVvXq1TFw4EC51xYtWqRQrlKlShg3bpz497179wr/oB/h8ePHOHHiBACgQoUK2LZtm9In45s3b45t27aJw5IdP34cz549kyuzevVqcXnixIkYOXKkwjFhZGSE//znP+Kwhu/evVPoRXrw4EG8evUKANC+fXvMmzcPenp6cmXq1auHLVu2aDQvGJV/6enp4vmqq6uLTZs2KfS41NXVxYwZM9C7d2+N6124cCF69+4NIyMj1KpVC6NHjxaPtePHj4v3FC8vL7Rv315pHW3atJEbhrRg7zHZ+623t7fSHk7u7u7itUlbW1vufpuUlCT23JX2NCvI0NBQrndYcV43PD09MXXqVFSpUgVmZmbo3bu3OMxrcd7DpXOwAcDy5cuVzh84YsQIjB8//oPinzJlCry8vGBiYgJzc3MMGzZM7D174MAB8VrToEEDbN68WWmPnr59+8r1Kl2/fr1cL2LpcJLZ2dlKhxWW3vtkh8dVNsShbK+0jh07ftDnLMjGxgarVq2CoaGh3OuVKlUS57UDoDTe0jRz5kxxDj6pSpUqYcSIEXKvzZgxQ+EY0dfXx+TJk8W/C54HgiCI9yktLS1s3LhR6RC7lSpVgo+Pj/h9Xbx4UezJSEREREREVNyY9CIqJjo6OuLy7du3VZbz8vKCv78/QkND8c033ygtY2FhgU6dOqmsQ7ZR4vXr13LvHT58WGxU/Prrr9XOw2JhYSHOPZGUlCQOnQjkN7xJ/65bty66deumsh4Aco1mBedeklW9enUx8aeqHmkDaVBQkNI5XzRx5swZcXny5Mly34+sKlWq4Ntvv1VZT3Z2NiZOnIgJEyZg7NixaocX0tPTkxvSSDq0U1GoG2awbdu24vLbt2+LvI2i6NWrF4yMjJS+d/PmTcTGxgLIb4js27evynoqVqyIKVOmiH+rS3pZWloqHa4PAKysrMTl6tWrq5x3R3bIzYLzuxW3kydPinOn9O/fX+1wn1ZWVujfvz+A/MZD2f0QFRUlNu6bmpqqnWtLW1tbHH4RyL8OyAoICBCXZROABdWrVw99+vRR+T59Pq5evSoOD9epUyfY2NioLDtt2jS1Q8BKmZqa4quvvlL5frt27TBjxgx4enpi8ODBauuSTV4XvJZqer+dPn06zp07h9DQUHTp0kV8XVf3/0f3fvz4scpr9Zdffoljx47hxo0bhSbmP8TQoUNVvldc9/Dw8HBERkYCyE+we3h4qKxn/PjxGs3ZBuQnNoYMGaLyfWnCH8i/96pLog8aNEi8Z8bGxopDUgKQm69SOjyhlGwirHv37mJSUNkQjdKkl6mpKVq1aqUyFk14eXmpPA9atWolftaYmJiP2k5xMjQ0VDmPpez9U09PT2U52cRiYmKi3Hs3b94U521r166d0vn3pHR1dTFmzBjx72PHjhX+AYiIiIiIiIqASS+iYmJvby8ub9u2DVOnTkVQUJDCk9cmJiaoV6+eQi8LWbLzIykjOx9RwcY62Ynd1TViKtuW7NPJsvUo66VSUPPmzcXPpO4p5y5duqhMQAFA5cqV0bJlSwD5n01dbzhVcnJyxMYzfX19ODo6qi2vrAeblL6+PgYMGIAffvgB06dPV1vPq1ev5L6PoibsdHV11e7zypUri8tZWVlF2kZR2draqnwvJCREXFa3T6U8PDzEY+bBgwfi3GsFSefBUka2B4G6fSY7z0t2dnahsX0M2YZX2YZ2VWQTyjdv3hSXZfeni4uL2CNMlZYtW6JGjRoA8pOhL168AJD/eaU9IAwMDOSuVcqoaxynz8fFixfF5cJ6wNSsWVPlPEeyWrZsqTY5Zm9vjzFjxmDBggViT09l0tPT5XrKSpNAUrKJ/0WLFmHevHm4du2awrltbm4OCwsLhXuOkZGReL14+fIlBg4cCF9fX6XJCmtraxgbG6uM9UMZGhrKJRsKKq57+Id8vxUqVICzs3Oh2wLyHyBQtT8yMzPF3jvSORgLIzsXmOz1z9HRUbzmFUx6hYaGir+tHBwcxETLvXv3kJmZKZZLTEzEgwcPAOTPU6Xut4cmWrdurfI9HR0dsdf4xzzwUtysra1V3jtkf0taWloq9IaXku3ZVvB3jezx2rRp00LjkU08lrUecURERERE9PnQLbwIEWmicePG6N27t/jk6smTJ3Hy5Eno6+vDzs4Ozs7OcHNzk3tiVhXZpIYysg03eXl5cu89ffpUXFbXM0QZ2V5jsvX4+/srDBmnTlJSEjIyMpQ2oKhLYEg1bNhQbEgp2JNNEwkJCWICpbAEo7SMvr6+xsmQN2/eICIiApGRkYiKisLTp0/x8OFDhVilvX0+lLGxsdoEh+x+Lfj9l7Qvv/xS5XvR0dHisibHi4GBASwtLfHkyRPk5OQgJiZGaa+oatWqqaxDtoFdXcO0lpZWofEUlw/dD7JlZBv6P7QeIP86JO1t9/LlS9SrVw9v374Vj21LS8tCG37VNcjT50P2WNPkO7e2ti70IQR11wdlMjIy8OLFC0RFRSEqKgovXrxAeHg4Hj9+LJfoKngtdXd3h729Pa5fv47c3Fzs27cP+/btg6GhIezt7eHs7AxXV1dYWFio3PaMGTMwduxY5Obm4vnz5+JQe/Xr14eTkxNcXFzg4OBQaLL5Q9WuXVttYrC47uHS4RsBzb/fkydPFlpO3XccFxcnJkUsLS01GipVdkhN2WPS0NAQbdu2xZUrV3D//n0kJyeL13jp0IYVKlRAixYtYGdnh9OnT0MikeD27dvisJmXLl0Sj52PHdoQAMzMzNS+L+1B+Knvy+qou3/K3hfV3T81PV63b9+O7du3axxbWeoRR0REREREnxcmvYiK0eLFi2Fqaoo9e/YgNzcXQH4vi2vXruHatWtYsWIF6tWrhwEDBmDEiBGoUKGC0npke6V8qOTk5GJZ92Pqka6vLOlVWKMRALk5sxISEgDkN44U1iDXqFEjuLi4yA35p8kT+tra2jAxMcGbN29UlklNTcUff/yBw4cPq03E6ejoiN99Ual62loT+/fvF4csU2X06NFFrl/d/kxKShKXC5snTUr2u1Z1zGm6P4ryFH9qair27duntkzNmjXRvXt3jeuU3Q/K5n8rSLbM+/fvi1xPwXLS/Sl7PmgyhJkm5yiVfx96XGhyTms6RN6ZM2ewc+dO3Lp1S+X1Ut21VFtbGxs2bMCCBQvkhtNNT09HUFAQgoKCsHDhQtjY2GDw4MEYOHCg3JCGAODk5IT169dj3rx5ctf+58+f4/nz59i5cyeMjIzQpUsXeHt7yyXkP+a6Udg+Kq57eEl8v4XVVVzXLCkXFxdcuXIFeXl5uH79ujjsszTpZWtrC319fbneqzdu3BCTXtLhHnV1dTXuyaaOqqF9C3Py5MlCEzyenp5Frl+dkrx/AsV3vBIRERERERUnJr2IipG+vj7mzp2LUaNG4cSJEwgMDMSdO3fkGu5evHiBlStXYv/+/di1a5c4HFlxkX06fsqUKRo9aS0lnRcDgFzMHh4eaof1UUZV4q6wXlcFty0tHxUVheXLl6tdr1+/fnBxcZHrsaXpE9fqemU9ffoU3t7eCo1Wenp6qFu3Lho3bowWLVrAyckJv/32m9J5RT6VzZs349WrV2rLfEzSS13DWFF6tsl+16p6Y5VkL62kpKRCjyt7e/sPSnp9KNljVPazyu5PTfeBsv35oftPk3OUyj/Z66Qm564mZQomlgqSSCSYNWuW3LxPUtWrV0ejRo3QrFkz2Nvb49WrV/jll19U1mVsbIyVK1fiu+++w8mTJxEYGIgHDx7IxXn//n38+uuvOHToEPUjqoUAACAASURBVP7880+FpIK7uzucnJwQEBCAs2fP4vLly3KJ59TUVPj5+eH48eNYtGiROE/hx1w3CttHxXUPlx2Grri+X0B9/EW5Zqm7R7u5uWHp0qUA8oc47NSpEzIyMsQeh9JkV+PGjWFiYoL379+LQ7kKgoArV64AyB8CsjiHqPxQe/fuLfR3QdeuXUsk6VXSvZxl7zn9+/fXaDQDIiIiIiKiksakF1EJqFmzJry9veHt7Y3U1FTcuHEDV65cQWBgoJiQiIqKwty5cz9oKBhNmJiYiE94f/XVV6hXr16R6pFtIGrYsOFHJUpkFdYLCZB/+ld2zglNadJ7SJYgCCrjys7OxsSJE8WEl6WlJUaOHIm2bdvC0tJSoQGw4Bxu/yayPQVkn/hXR7acpr1EyjpjY2Ox58j79+8LHa5Udh/InnfFtT9lzyHZBn1VNDlHqfyTPb40OS6Ko1fGpk2bxISXvr4+hg0bBnd3dzRt2lShwX/Pnj0a1Vm/fn1MmjQJkyZNQmJiIkJCQsT7rbSncFhYGJYvX44FCxYorK+vr4+vvvoKX331FfLy8vDgwQNcvXoVFy5cwM2bNyEIAiQSCX7++WfY29ujVq1aH7kX1Cuue7iqHqSqFMd5L7vNolyzCiamLC0tUbduXURGRorzdN66dUtM6Dk4OADI7/nXtm1bnDt3DqGhocjOzsbjx4/F7784hjYk5WS/M1tbW3h6epZiNERERERERPmY9CIqYUZGRujYsSM6duyIuXPnYs+ePVi4cCGA/KF33r59W6TEjioWFhZig9ndu3cLbTDLyMiAlpaWwlCLsnOh3L17V6Ntv3v3rtCh0Z49e1ZoPY8fPxaX69SpAyC/cevRo0caxWFhYQEDAwNkZWUhIiICEolEbe+V6OhoZGVlKX3vzJkziIiIAADUqlULBw4cUPvEeGJiokYxlpTAwMBS27bsMfPo0SN89dVXasunp6eL885oa2ujdu3aJRqfMl9++aXGx5Wm6tSpIya9Hj16JA61pUp4eLi4LLsPC+5PTciWk65ftWpVcc66iIgIZGdnq52n6Pnz5xpti8q3Bg0a4M6dOwDyr8vNmzdXW16Ta7c6WVlZ2Llzp/i3j48POnfurLL8u3fvxGVNeyFVrlxZTGDl5ORg9erV2Lp1KwDg2LFjmD9/vtqeL9ra2mjWrBmaNWuGsWPH4tmzZxg1ahRiY2MhkUhw4sQJjBkzpkSuG1LFdQ+XHY5Rdt4lVTQpU5hatWpBT08PEokEERERyMrKKrSnmqrrn5Srqyt27tyJ58+fIy4uTuwxJZ3PS8rBwQHnzp1DVlYW7ty5I84LCuT3GCtNu3btKtXtl6SCvxULS3rl5uYiLS2tVHveERERERHR50/1zMRE9EHmzp2LAQMGoH379ioTKFpaWhg+fLhcY1RcXFyxxmFnZycuazIp/YoVK9CyZUs4OTlhx44d4uuywxneuHED8fHxauu5d+8e2rVrh1atWmHYsGEqy126dEltPW/evBGTbDVq1ECTJk0K/QwF6enpwdbWFkD+EE/SJ8RVUZcokg6jBABdunRR21ATHR0tN7RgWZrM/lOQPWbOnj1baPmAgABxaKQmTZqonOOuvJHdD2fOnCm0vGwZ6XFbsJ6LFy/KDUenzK1bt8Rkm6mpKSwtLQHknw8dOnQAkH8+XLhwQW09hZ2j9HmQnQcpKChIbdnExES5a2FRPH/+XOxNZGpqqjbhBQAhISHismzSKzU1FdOmTUOfPn3UDjuqq6uLqVOnwtDQEEB+ckja4+nYsWMYOXIknJ2dceTIEZV1NGjQAMOHDxf/Lu77tTLFdQ+X/X4LO+dzcnLE+a8+hoGBAWxsbMQ6NXkIQ9X1T8rV1VVcDg4Oxs2bNwEArVq1kkveS3t9AcD169fF61jdunVRv359pdsu6aH/iqqsxqWM7H0qICBA5e9fqbNnz6Jt27Zo27Ytpk+fXtLhERERERHRvxSTXkTF5Pnz57h37x4SExPVNvjn5eXJDedTvXr1Yo2jT58+4nJQUBCCg4NVlo2MjMTBgwcB5CebmjVrJr7XsGFDsfFKIpFg5cqVKusRBAE+Pj4A8nvvSBvblbl165Y4Cb0y69evFxMh3bp1K3LjT//+/cXlzZs3q+wpkJaWhr/++ktlPbLzosj2PFCm4BwvsnOz/Bu0a9dOPJ6fPn2qtjE5IyMD69evF/8uyTmzPrU+ffqIx62fn5/aHjKPHz+W20+y+6F+/fpiT4akpCRs2bJFZT15eXly52j37t3lzp1u3bqJy+vWrVOZQEtISMDevXtVboc+H507dxbnXjx37hyePHmisuyWLVvkroVFIbt+Wlqa2sbxoKAguTmQZNc1MjLCP//8g/DwcDx79gy3b99WWU9GRoa4nQoVKojD72VlZSE4OBjx8fE4fvy42rhle+9Wq1ZNbdniUFz38DZt2og9pcPCwtQmtfbu3SsOBfixevfuLS6vX79e7fe8b98+sSe1qampmJyXZW9vLyYuL1y4gLCwMPF1WVZWVmJPc+kwh4D6oQ21tf//v0Gyc1OVNtm5M8tSXMq0b98e5ubmAPLPlc2bN6ssm52dLd73k5OT5R4AIyIiIiIiKk5MehEVE9kky/z585U+FS8IAlasWCE2LtnZ2RXr0IYAYG1tLTZwC4KA77//XmnPjcjISHz33Xdig5SDgwPatGkjV+b7778Xl48ePYolS5YoNJZnZWVh8eLF4oTxenp6GDt2rNoYp02bJjZcSeXl5WHz5s3w9fUFkN8ANn78eE0+slLdu3cXG1Ru3bqFn376SaHx7d27d/juu+/kemcVZG1tLS77+/uLw4HJSklJwY8//ojTp0/Lvf5vm99LT09P7jubN2+eOH+PrLdv32Ls2LFiY6elpSWGDh36qcIscQ0bNhTPwczMTIwZMwYPHz5UKHf//n14e3uL51Tv3r3lhusCgEmTJonLGzZswI4dOxQSuNKeL9IeEGZmZpgwYYJcmV69eokN4o8ePcKUKVOQmpoqVyY+Ph7jxo3TeC4eKt+++OILeHt7A8hPKo0fP14hQSsIAnbs2CHXg6io6tevLw4zK5FI8J///EfhWBYEAUeOHMHUqVPlXs/MzJT7W/Z+O336dLx48UJhexKJBL/88ouYNOjcubOYCO7WrZuYSLl8+TLWrl2r9CGF0NBQMQmsra2NTp06fdBnLoriuodraWnJXT+mT5+OW7duKdRz8uRJ/P7778UWf//+/cWhap8+fYrx48crHfb36NGj4lDPADBjxgylQyHq6+ujXbt2AIDTp0+Ln7dg0ktLS0t87eHDh+L3rm5oQ2nSF4A4b2dZIBtXXFxcme41rq+vL3e/2bRpE7Zu3aoQc0pKCmbMmCEm101NTeV6URIRERERERUnzulFVEz69++Pffv24e7du0hOTsbgwYPh6OgIa2trVK5cGQkJCbhy5Yr4H34DAwPMnj27RGJZsGABwsPDERERgeTkZHh7e6N169Zo27YtdHV18eTJEwQEBIiNfJUrV8bSpUsV6nFzc8OoUaPwxx9/AAD++usvnD59Gu7u7qhWrRpiY2MRFBSE2NhYcZ2ffvoJdevWVRmbnp4eEhISMGTIELi5ucHGxgYZGRkICgoS942uri7mz59f6Pxg6ujr62PJkiXw8vJCVlYW/Pz8cPXqVXTu3BlVq1ZFdHQ0zpw5g/fv36NixYpigqpgz7KePXti7dq1SExMRFZWFoYOHQoPDw80atQIubm5iIiIwIULF8T1pfOZAPhXJg+GDh2KkJAQ+Pv7IysrC9OmTcP27dvh6OiIL774AhERETh37pyYcPniiy/g4+MjNkB/LhYsWICHDx8iIiICr169woABA+Dk5CTOm3Tv3j1cunRJbJitX78+fv31V4V6XF1dMXr0aGzfvh15eXlYunQp9u3bB1dXV5iamuLVq1c4d+6c2Kisp6eHZcuWKfRI0dHRweLFizFixAgkJycjMDAQXbp0QdeuXVG9enVERkbizJkzSE1NhaWlpZiQpM+bt7c3zp8/j7CwMERHR6NPnz7w8PCAtbU10tPTcfHiRXHuqgoVKojJJ9keMpoyMjJCv379sH//fgDAn3/+iatXr6J9+/YwMTFBXFwcLl++jOjoaADqr6Xe3t44duwYXr16hVevXqFHjx5wcXFBgwYNxLqCgoLEukxNTTFlyhRx/UqVKmHq1KlYvHgxgPyE8rFjx+Do6IiaNWtCIpHgwYMHuHTpknif9PLyUjlMXnErrnt4nz59cObMGZw7dw5JSUkYNmwYXFxc0Lx5c+Tk5CAkJERMhMneB4vy/UpVrFgRq1evxjfffIP09HTxvuvh4YF69eohPT0dV65cwf3798V1evbsiUGDBqms083NDYGBgeL1suB8XlIODg5yD58YGRkpPMwjS3YeyWPHjqFSpUqoWrUq2rRpo3a9klapUiUYGxsjOTkZ0dHRmDRpEmxtbVGtWjX07du31OJSZdiwYbh+/Tr8/f0hCAJWrlyJgwcPKtynpOexjo4Oli5dynm9iIiIiIioxDDpRVRMdHR0sGXLFkyYMEHsDXT16lWl80mZm5tj6dKlaNmyZYnEYmJiAl9fX8yYMUMc0ujmzZtiTxBZVlZWWLNmjVzjj6xZs2ahSpUqWLt2LbKyshAbG4v//e9/CuUqVqyIOXPmFDqJ+YgRI/Dw4UMEBwcjICAAAQEBcu9XqlQJq1evhpOTk6YfVyVbW1usW7cOM2fORFJSEmJjYxUmlK9WrRrmz58vPqksO0eINJ5169ZhwoQJSE5ORm5uLs6cOaN0riZPT080adIEv/32G4D8njwDBgz46M9R3vj4+KBGjRrYtWsXcnNzcf/+fbkGTikrKyusXbsW9erVK4UoS5axsTF8fX0xffp0XLlyBbm5ubhw4YLSuXW6deuGxYsXw8jISGldM2fOROXKlbFmzRpkZ2fj+fPneP78uUK5WrVqYfXq1SqvK40bN8aePXswduxYxMTEICEhQeFctrCwwOrVq8tkwyoVP319fWzduhXfffcdbt68CYlEAn9/f/j7+4tldHR0MHXqVFy6dEmcZ6vgdVJTc+bMQUREhDh04aNHj8SkmiwrKyssXboUQ4YMgUQiQXh4OHJzc8Vh34yMjPDHH39g/PjxePHiBXJzc3H+/HmcP39eoS5LS0usWrUKFhYWcq97eXnh/fv32LRpE3Jzc/Hy5Uvs27dPYX0dHR2MGDECs2bNKtJnLorivIf7+Phg+vTpOHv2LPLy8hAUFKQwh9uIESOQlpYGPz8/AEX/fqVatGiBPXv2YMqUKYiKikJqaiqOHj2qUE5HRweTJ08utFe37LxegOJ8XlLSHmFSTk5OYu9CZaytrdGyZUuEhoYiLy9P/H0wYsSIUk16AcCgQYOwfft2ABB/K1lbW5fZa7OPjw9q1qyJnTt3ig8EKXt4wtTUFIsXL4a7u/unD5KIiIiIiP41mPQiKkaVK1fG3r17cfr0aZw6dQoPHjzA27dvkZOTg8qVK6NBgwZwd3dHv379VDZwFxczMzNs374dV69exfHjx3Hr1i3Ex8dDIpHAzMwMTZs2xVdffYUePXqobRQC8p+q79mzJ/bt24crV64gKioKKSkpqFixIiwtLeHk5IQhQ4agRo0ahcZlaGiIP/74AwcPHsShQ4fw9OlT5OTkwNLSEh4eHhg+fDgqV65cXLsBrq6uOHnyJHbv3o3AwEC8evUKEokEFhYW6Ny5M0aNGiU3l0nFihUV6mjTpg3+/vtv7NixQ+yJkJ2dDSMjI3z55Zdo2bIlBgwYABsbG8TFxWHBggXIy8vD6dOnMXv27I9uQCxvdHR0MGfOHAwePBj79+/H1atXERMTg4yMDJiZmaFZs2bisaer+/nehszMzPDHH3/g8uXLOH78OG7evCleD2rWrInWrVujX79+cHBwKLQub29v9OjRA76+vuIxmJKSAmNjYzRp0gSdO3dG//79UaFCBbX1WFlZ4cSJE9i7dy/8/f3x7NkzCIKA2rVro0uXLhg9enSZHkqLip+pqSn27NmDY8eO4dixY3j06BGSkpJgZmYGe3t7fPvtt2jWrJncAwrKrpOaMDQ0xI4dO+Dn54e///4b4eHhSElJgYGBAczNzdG4cWN4eHiI1wYHBwdcvnwZKSkpOH/+vNzwgpaWlmLM586dQ3h4OBITEyEIAqpWrQpra2t06tQJvXv3VnkNnjx5Mrp27YoDBw7g5s2bePnyJdLT02FkZITq1aujQ4cO6NevH6ysrIr0eT9Gcd3DDQwMsH79egQGBuLw4cMIDQ1FYmIiKlWqhFatWmHEiBFo3749pk+fLq5T1O9XVtOmTXHy5EkcPXoU586dw4MHD5CYmAhdXV3UqVMHHTp0gKenp9o5QKVq1KgBa2trMUFacGhDqQYNGsDc3Bxv3rwBoH4+LyC/R9vWrVvh4+ODoKAgJCQkwMDAQGE4zdIwY8YMmJqa4vDhw+IQzIIgQBCEIs91WpJ0dHQwe/ZsDB48GAcOHEBwcDBiYmKQmpoKIyMjNGjQAG5ubhg0aNBH9eInIiIiIiLShJZQcEIFIqJi5ufnhzlz5gDIn6No8uTJpRyRvLCwMHFoJVdXV2zZsqWUIyIiKlt69OiBp0+fAgCCg4OL9eEEKn1jx44Ve6IeOHBA6fCBREREREREROXB5/uIPRH9qx0/fhwZGRmwtLSEra2t2t5WssNrfY5D7RERFfT06VNcuHABlpaWaN68ucI8cLLS09Px8uVLAPlD7zHhVfbFx8fj8OHDqFevHpo0aaIwvKMsQRDEOTUB3geJiIiIiIiofGPSi4g+Szdu3BDnZ9mwYYPcsFiy8vLysHfvXvHv1q1bf5L4iIhKU0pKCpYvXw4A8PDwwMaNG1WWPXDgALKysgAAdnZ2nyQ++ng+Pj4A8ufzUzanllRAQABev34NIH8I1EqVKn2S+IiIiIiIiIhKgnZpB0BEVBJk50ny8fFBfHy8QpnMzEz88ssvuH//PgCgTp06cHV1/WQxEhGVFhsbG3FuyaCgIJw+fVppucDAQKxcuVL8e8SIEZ8kPvo41apVQ/369QEA4eHh2LlzJ5SNaH7nzh389NNP4t9eXl6fLEYiIiIiIiKiksCeXkT0Weratas48f2zZ8/g4eEBd3d3WFpaQldXF69fv0ZQUBASExMBALq6uli6dCkMDAxKOXIiopKnr6+P8ePHY+XKlcjNzcX333+Pli1bomXLlqhcuTISEhJw584d3L17V1zn66+/RocOHUoxavoQkydPxtSpUwEAixcvxv79+2Fvbw9zc3MkJSXh4cOHCAkJEcu7uLiI81sSERERERERlVdMehHRZ0lXVxfbtm3DpEmTEBoaiuzsbPj7+ystW61aNaxevZpDGxLRv8qYMWOQmpqKrVu3Ijc3F6GhoQgNDVUop6Wlhe+++w6TJk0qhSipqLp37453797h999/R1ZWFp48eSI3d5eswYMH45dffvnEERIREREREREVPya9iOizVa1aNfj6+uLs2bM4efIk7t69i4SEBGhpaaFatWqwsLBAjx490K1bNxgaGpZ2uEREn9zUqVPRq1cvHDhwANevX0dUVBQyMzNhZmaGGjVqoEOHDhgwYADq1KlT2qFSEQwbNgxubm44cOAAgoOD8eLFC6SlpcHExATVq1dH69atMXDgQDRu3Li0QyUiIiIiIiIqFlqCsgH+iYiIiIiIiIiIiIiIiMoR7dIOgIiIiIiIiIiIiIiIiOhjMelFRERERERERERERERE5R7n9CIiIiIiIqLPmiAIyMrKQnJyMlJTUyGRSJCXl1faYRERERER/etpa2tDT08PRkZGMDY2hoGBAbS0tIpcH+f0IiIiIiIios9WTk4OXr58iczMzNIOhYiIiIiIClGhQgVYWFhAV7dofbaY9CIiIiIiIqLPUk5ODqKiopCVlVXaoRARERERkYYMDAxQp06dIiW+mPQiIiIiIiKiz44gCIiIiJDr4WVsbAxjY2MYGhpCW1v7o4ZNISIiIiKijyMIAvLy8pCeno7k5GQkJyeL71WoUAGWlpYf/Judc3oRERERERHRZycrK0su4VWrVi2YmJiUYkRERERERCRLS0sLOjo6qFSpEipVqgQjIyO8fv0aAJCZmYmsrCxUqFDhg+rULolAiYiIiIiIiEqT7FOixsbGTHgREREREZVxJiYmMDY2Fv+W/U2vKSa9iIiIiIiI6LOTmpoqLsv+x5mIiIiIiMou2d/usr/pNcWkFxEREREREX12JBKJuGxoaFiKkRARERERkaYqVqwoLsv+ptcUk15ERERERET02cnLyxOXtbX5X18iIiIiovJAR0dHXJb9Ta8p/vInIiIiIiKiz5qWllZph0BERERERBr42N/uTHoRERERERERERERERFRucekFxEREREREREREREREZV7THoRERERERERERERERFRucekFxEREREREREREREREZV7THoRERERERERERERERFRuadb2gEQERERERERESkTFhaGc+fOITg4GHFxcUhMTIShoSHMzMxQv359uLi4oFOnTjA3N1dYNyQkBF5eXsUSx6RJkzB58uRiqYuIiIiISg6TXkRERERERESlKDo6Grt370ZwcDDCwsKQnJwMAwMDWFtbo02bNvD09ESbNm1KO8xP6tWrV1i8eDECAgIU3nv//j3ev3+PiIgIBAYG4vfff8eoUaMwbtw4GBgYlEK0RERERFRWaAmCIJR2EERERERERETF6eHDh+JykyZNSjES1WJiYjBt2jQcOHAAubm5asva29tjzZo1aNeu3SeKrvTExsbi66+/xuvXrwEAFStWRPv27dGgQQMYGxsjOzsbMTExuH79OqKiosT1XF1dsWHDBujp6QHI378nT55UuZ0rV67gypUrAIBmzZqhe/fuKsu2atUKdnZ2xfHxiIiIiKgQH/Nbnj29iIiIiIiIiD6xo0eP4ttvv8W7d++gq6uLQYMGoU+fPrCzs0PVqlWRnp6Ou3fvIjAwEDt27MD169fRoUMHzJ49GwsXLoS29uc5RbcgCJg8ebKY8OrZsyd+/fVXGBsbKy1/8uRJzJ07F+np6bhw4QKWL1+OuXPnAgBq1qyJ0aNHq9xWenq6mPRq1KiR2rJEREREVD58nr+SiYiIiIiIiMooX19f9O/fH+/evUPXrl3x9OlT7N+/H8OGDUOTJk1gbm6OunXromfPnvDx8UF0dDRmzZoFAFiyZAnGjRuHz3XQlmvXriEsLAwAYGtrixUrVqhMeAFA9+7dsXLlSvHvvXv3IiEhocTjJCIiIqKyiUkvIiIiIiIiok/kzp078PLyQl5eHn755RecOnUKdevWVbuOoaEhli1bBn9/f1SsWBHbtm3D6tWrP1HEn1ZwcLC43Lt3b416tHl4eKB58+YAAIlEgqtXr5ZYfERERERUtjHpRURERERERPQJSCQSjBw5EhKJBOPGjcP8+fOhpaWl8fqdO3fGnj17AAA//fQTHj9+XFKhlpqkpCRxOT09XeP1HB0dUaFCBdSoUQOZmZklERoRERERlQNMehERERERERF9An5+fggNDYWlpSVWrlz5QQkvqX79+mH48OHIzMzEsmXLSiDK0lWtWjVx+ciRIxonsKZOnYrQ0FBcuHABgwYNKqnwiIiIiKiMY9KLiIiIiIiI6BPYuHEjAGDmzJkwMjIqcj2//vorgPz5qxITE4sltrLCxcVFXH769ClGjBiBS5cuIS8vT+16RUkgEhEREdHnh0kvIiIiIiIiohKWmpqKS5cuQVdXF8OGDfuouho2bAgXFxdkZmbi4sWLxRRh2dCiRQu4u7uLf4eFhcHb2xsdOnTAzJkzcfDgQURGRpZihERERERUljHpRURERERERFTC7ty5A0EQ0KxZMxgbG390fe3btwcA3Lx586PrKmtWrlyJ1q1by72WmJiIo0ePYu7cuejSpQucnJwwffp0HDlyRG4eMCIiIiL6d2PSi4iIiIiIiKiERUdHAwAaNWpULPVJ65HW+zn54osvsGvXLsyePRtmZmZKy7x58wZ///03Zs2aBScnJ8yePRuxsbGfOFIiIiIiKmuY9CIiIiIiIiIqYYIgAAC0tYvnv+HSegqb66q80tHRwbfffouLFy9i48aNGDhwIGrVqqW0rEQiweHDh9GzZ09cunTpE0dKRERERGWJbmkHQERERERERPS5q169OgAU23xUERERcvV+rvT19eHh4QEPDw8AwMuXL3H9+nWEhITg6tWrePPmjVg2JSUFkyZNwv79+2FtbV1aIRMRERFRKWJPLyIiIiIiIqIS1qpVKwD5c3tlZWV9dH03btwAAIW5rz53FhYWGDBgAJYvX45Lly5hz549cHJyEt/PzMzE+vXrSzFCIiIiIipNTHoRERERERERlTAzMzO0atUKmZmZOHTo0EfVFRMTg7Nnz0JHRwfOzs7FFGH5o6WlhTZt2mD79u349ttvxdfPnj2LtLS0UoyMiIiIiEoLk15EREREREREn8D48eMBAMuXL4dEIilyPStXrkROTg769Omjcp6r8ujEiRMYOHAgOnbsiFWrVn3QutOmTYOZmRmA/PnTXr58WRIhEhEREVEZx6QXERERERER0ScwbNgw1K1bF6GhoVi6dGmR6ggODsbq1auhra2N2bNnF3OEpSs9PR13797F69evxeEbNaWvrw9LS0vx7+zs7GKOjoiIiIjKAya9iIiIiIiIiD6BL774Atu3bwcA/Pbbb/jrr78+aP179+6hT58+yMvLw4wZM9C2bduSCLPU2NnZicu3b9/GgwcPNF43NzcX0dHRAAAdHR3Ur1+/2OMjIiIiorKPSS8iIiIiIiKiT8TDwwPLli2DIAgYOXIkJk6ciNTUVLXrCIKArVu3wtHREW/evEHXrl2xYMGCTxTxp9OgQQO0bt1a/Hvms3jCWwAAIABJREFUzJlISkrSaF1fX1+8efMGAODq6gojI6MSiZGIiIiIyjYmvYiIiIiIiIg+oVmzZmH16tXQ1dXFxo0bUb9+fcyZMwchISHIyMgAkN9z6eHDh9iwYQNatGiBsWPHIiUlBYMHD8aRI0dgYGBQyp+iZMybNw+GhoYAgCdPnqB///44deoUcnNzlZZPT0/Hhg0bsGjRIgBAxYoVMXXq1E8WLxERERGVLbqlHQARERERERHRv82UKVPg5uaGMWPG4MaNG1i2bBmWLVsGbW1tGBkZITMzU25eqho1auA///kPPD09oaWlVYqRl6zGjRtj48aNGDt2LLKzs/Hq1Sv88MMPMDU1RYcOHVCzZk0YGxsjOTkZz549w/Xr15GWlgYA0NXVxerVq2FlZVXKn4KIiIiISguTXkRERERERESloGXLlggJCUFwcDD++OMPXLt2DQ8fPkRycjIAwMLCAm3atIGnpyf69esHfX39Uo7402jfvj2OHDmCJUuW4PLlywCApKQknDhxQuU6NjY2mD9/Ppo3b/6pwiQiIiKiMohJLyIiIiIiIqJSoqWlBUdHRzg6OgIAsrOzkZ6eDn19fXGYv3+jBg0aYPv27QgNDcXZs2cRGhqKqKgovHv3Djk5OTAzM4O5uTlatWqFLl26wMHBAdranMGBiIiI6N9OSxAEobSDICIiIiIiIipODx8+FJebNGlSipEQEREREdGH+Jjf8nwMioiIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiIiIiIiIio3GPSi4iIiIiIiIiIiIiIiMo9Jr2IiIiIiIiI6P/Yu/ewreZ8f+DvziVSyKgwQyrkEKGNkso0TjPY2A7jEIZmGMef4zAzmz2MMdhKhfkx2NEUxjDjNFJJQylpSuWQhqiUqHR4dHq6f3+43Nvzq3Tu6a7X67q6rnWv9Vnf9VnreZ7rWqv3vdYCAICSJ/QCAAAAAACg5FWv7AYAAABgczZx4sT06dMnw4cPz1tvvZV58+alZs2aadasWVq3bp3jjjsuhx12WKpUqVLZrW5wY8aMyUsvvZShQ4dm+vTpmTlzZrbYYos0aNAgu+66aw477LAcccQRadiw4RqNv3Dhwhx99NGZPHly/ud//idt2rRZx3sAAMCGVKVQKBQquwkAAABYl95+++3i9B577FGJnazYO++8kyuuuCLPP//8SmtbtmyZm2++Occdd9wG6KzyTZkyJTfffHMGDBiw0to6derk3HPPTdeuXVOrVq3V2s6vfvWr9OvXL0mEXgAAG4m1OZd3pxcAAABsQIVCId26dcu1116bhQsXrtI648aNy/HHH58f//jHuffee7Pllluu5y4rz7Rp03LGGWdk6tSpSb4KtQ4++OA0bdo09erVy6JFi/LJJ59k+PDh+eijj/Lll1+mZ8+eGTt2bHr27JkaNWqs0nZ69uxZDLwAANg0CL0AAABgAykUCrn66qtz++23r9H6jz76aP71r3/l73//e7baaqt13F3lKxQKufjii4uB17HHHptf//rXqVev3nLrn3vuuVx//fUpKyvL4MGDc9ttt+X666//1m0sXrw4v/vd79K7d+913j8AAJWramU3AAAAAJuLnj17rnHg9bWhQ4fmxz/+cTbFtxUMGzYsY8aMSZK0atUqv//971cYeCXJ0UcfXeF4/ulPf8rnn3++wvopU6bkzDPPFHgBAGyihF4AAACwAUyYMCFXX331Ohnrb3/7Wx5++OF1MtbGZOjQocXpH/3oR6ladeX/bdGpU6fsvffeSb66i+u1115bpmbevHm58847c9RRR2XUqFFJku222y577bXXOuocAICNgdALAAAANoBrr702X3755Tob78orr8yCBQvW2Xgbg9mzZxeny8rKVnm9Qw45JLVr184OO+yw3GPyxBNP5L777iu+Q61NmzZ5/PHH06xZs7VvGgCAjYbQCwAAANazyZMn5+mnn16nY37++ed5/PHH1+mYlW377bcvTj/11FOrHOpdfvnlGT16dAYPHpyTTz55hXUNGzbMb37zmzz88MNp3LjxWvcLAMDGRegFAAAA69njjz+e8vLydT5unz591vmYlemwww4rTr///vs588wzM2TIkCxduvRb16tSpcq3Lt9hhx1y3XXX5cUXX8zJJ5+80noAAEpT9cpuAAAAADZ1b7zxxnoZd8SIESkUCptMiLPPPvukY8eOGThwYJJkzJgx+clPfpJtttkm7dq1y0EHHZQDDzww3/3ud1dr3COPPHJ9tAsAwEZG6AUAAADr2bhx49bLuJ9//nmmT5+eHXbYYb2MXxluv/32nH/++Rk5cmRx3syZM/P0008XHxHZsGHDtGnTJu3atcvhhx+e+vXrV1a7AABsRDzeEAAAANaz+fPnr7exy8rK1tvYlaFu3brp3bt3rr322jRo0GC5NTNmzMgzzzyTa665Jm3bts21116badOmbeBOAQDY2Ai9AAAAYD2rXbv2ehu7Vq1a623sylKtWrWcc845eeWVV9KrV6+cdNJJady48XJrFy9enL/85S859th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QkBD17t1b8fHxzi4LAAAAAIAsI/QCgAfYmDFjNHXqVHl4eGjVqlXq2LGjs0tCNmvTpo1CQ0OVL18+zZo1S2+//TbBF7LF33//rTZt2ujSpUvG68zb29vZZSGbNGzYUJs3b1bhwoW1atUq9e/fXwkJCc4uCwAAAACALCH0AoAH1LRp0/Thhx/Kzc1Ny5YtU+fOnZ1dEnJI27ZttWrVKnl4eGjq1KmaNGmSs0uCyUVERCgwMFDnz59XixYtFBoaKi8vL2eXhWwWEBCgDRs2yMfHR0uWLNHQoUMJzQEAAAAApsaeXgDwANq4caM6deoki8Wir7/+Ws8//7yzS0IuWL16tZ555hlZrVatWbNG3bp1c3ZJMKG7d+/qySef1P79+9W4cWNt3rxZhQoVcnZZyEFhYWHq0KGDYmJiNHXqVL355pvOLgkAAAAAgPvCSi8AeMAcP35cwcHBslgsGjNmDIHXQ6RHjx6aOHGiJKlPnz767bffnFwRzMZqtWrIkCHav3+//Pz8tG7dOgKvh0DLli311VdfSZLefvttbdy40ckVAQAAAABwf1jpBQAPkBs3bqhJkyY6duyYnn76aa1atUqursxveJhYrVY9//zzWrp0qSpWrKgDBw6oePHizi4LJjF16lS99dZb8vb21p49e1S3bl1nl4RcNG7cOE2YMEGFCxfWgQMHVLVqVWeXBAAAAABAphB6AcADIiEhQV26dNEPP/yg2rVra8+ePfLx8XF2WXCCmJgYPfHEEwoPD1fr1q21ceNGeXh4OLss5HEbNmxQUFCQLBaLVq5cqZ49ezq7JOQyi8WiZ555Rt99952qVaumffv2qUiRIs4uCwAAAACADGP6PwA8IEaOHKkffvhBxYsXV0hICIHXQ8zLy0tr1qxRqVKl9OOPP2rEiBHOLgl53LFjx/Tss8/KYrFo3LhxBF4PKVdXVy1evFi1a9fWsWPH9NxzzykhIcHZZQEAAAAAkGGs9AKAB8CSJUvUp08fubu7a/PmzXryySedXRLygL179+rJJ59UbGysvvzySw0aNMjZJSEPun79upo0aaLjx4+rR48eWrFiBW1RH3KnT59Wo0aNFBkZqbfffluTJ092dkkAAAAAAGQIoRcAmNzZs2dVq1YtRUdHa9asWRo6dKizS0IesnDhQr344ovy8vLSkSNHVLlyZWeXhDzmueee07Jly1SnTh3t3r2bVaKQJO3YsUNt27ZVfHy81q1bp6CgIGeXBAAAAABAupjGCwAmZrVaNWjQIEVHR6t79+4aMmSIs0tCHtO/f3/17t1bMTExGjBggCwWi7NLQh6yevVqLVu2TAUKFNDq1asJvGBo1aqVJk2aJEkaPHiwrl275uSKAAAAAABIH6EXAJjY3LlztXnzZhUrVkyzZ8+Wi4uLs0tCHjR9+nSVKlVKO3fu1MyZM51dDvKIyMhIY2Xoxx9/rEqVKjm5IuQ1b775ppo2baqLFy+yNyAAAAAAwBRobwgAJmXf1vCbb77Rc8895+ySkIeFhITo6aefps0hDM8++6yWL1+uJ598Ulu3bmUfL6Tq2LFjqlevnu7evUubQwAAAABAnsenGwBgQsnbGj777LPOLgl5XLdu3WhzCMOqVau0fPlyFShQQPPmzSPwQpqqVaumDz74QBJtDgEAAAAAeR+fcACACc2bN89oazhr1izaGiJDaHMIKbGt4bBhwyQltjWsWLGikytCXvfGG2+oWbNmtDkEAAAAAOR5tDcEAJM5f/68HnvsMdoa4r7Ytzn87bffCDweQrQ1xP2gzSEAAAAAwAz4lAMATGbcuHGKjo5Wt27daGuITOvWrZuee+45xcTEaPTo0c4uB7ls+/btWr58uby9vWlriEyxb3P42muvKTY21skVAQAAAACQEiu9AMBEfv/9d9WuXVuurq76448/VLlyZWeXBBM6d+6cqlSponv37ik8PFwNGjRwdknIBVarVU2bNtX+/fs1YcIEjRkzxtklwWTi4+NVu3Zt/fnnn/rss8+MNpkAAAAAAOQVTO8FABMZPXq0LBaLXnrpJQIv3Ldy5crplVdekSSNGjXKydUgt4SEhGj//v0qWbKk3nzzTWeXAxNyd3fXhx9+KEmaMGGCbt++7eSKAAAAAABIipVeAGAS+/btU9OmTeXt7a2TJ0+qdOn/x96dx1VZ5v8ffx9BEE0xctec3HcTXNC0XMs11Kzcvqa5pThWajWVtlqalU5jpWnuVmoqKuUWrrkAouBe7lmKK4Iishzg/P7od5gcrQSB69zwej4ePsYBPOfl8eZm5v6c67rLmk6ChcXExKhy5cq6du2aNm3apNatW5tOQg5KTU1V/fr19dNPP+nTTz/NGHoCmeVwONS0aVPt2rVL7733HtukAgAAAABcCkMvALAAh8Oh1q1ba+vWrXr99dcz3mmf38XFxWn//v2Kjo7WuXPnFB0drbi4ONntdklSwYIF5e3trXLlyqls2bIqV66c6tWrJx8fH8PlruH999/XuHHj1KRJE4WFhclms5lOQg6ZO3euBg4cqEqVKunnn3+Wh4eH6SRjkpOTtW/fPu3Zs0dHjx7NOH+cO3dO8fHxSktLkyR5eHjIx8cn4/xRoUIF1a9fXw0bNlSVKlUM/y3M2rx5s9q0aaNixYrp5MmTuu+++0wnAQAAAAAgiaEXAFjC2rVr1alTJ/n4+OjkyZPy9vY2nZTr0tLStH//foWHhyssLEzh4eE6cuSIMvtjzGazqVq1avL391fTpk3l7++vBx98UO7u7jlU7roSEhJUpUoVXbhwQcuXL9cTTzxhOgk5ICkpSdWrV9dvv/2mr776Sn379jWdlKvS09O1c+dOBQcHKyQkRAcPHlRqaupdPWbx4sXVpEkTde7cWV27dtU//vGPbKq1jg4dOmj9+vUaM2aMPv74Y9M5AAAAAABIYugFAC4vPT1dfn5+2rdvnz7++GONGTPGdFKuOnXqlL744gvNmTNHly9fzpHn8PHx0YABAzR8+PB8d6+0adOmacSIEapZs6YOHDiQL4d/ed2UKVM0ZswY1a9fX1FRUSpQIH/c0jUiIkIzZ87UypUrc+zc4VSvXj316dNHgwYNUsmSJXP0uVxFVFSU/Pz85OnpqaNHj6pixYqmkwAAAAAAYOgFAK5u9erV6tKliypUqKCjR4/Ky8vLdFKOS09P19q1azVt2jStW7dO6enpufK8NptNjz76qAIDA9WlSxe5ubnlyvOalJKSolq1aunkyZP69ttv9dRTT5lOQjZKSkpSxYoVdenSJa1evVqdOnUynZSjkpOT9fXXX2v69OnavXt3rj+/h4eHnnzySY0cOVJNmzbN9efPbb1799bixYsVGBiozz//3HQOAAAAAADKH2/1BQALmzZtmiTp+eefz/MDr5iYGE2aNElVq1ZVly5dtGbNmlwbeEm/3zvthx9+ULdu3VS5cmW9//77unjxYq49vwkeHh4aNWqUJGn69OmGa5Ddli1bpkuXLsnX11cdO3Y0nZNj0tPTNW/ePFWvXl2DBg0yMvCSfh8if/PNN2rWrJk6duyovXv3GunILePGjZMkLViwQPHx8YZrAAAAAABgpRcAuLSTJ0+qatWq8vDw0JkzZ1SiRAnTSTlm9uzZeumllxQXF2c65SbFihXTBx98oGHDhslms5nOyRFXr15V+fLllZCQoMOHD6tWrVqmk5BNmjVrprCwMM2aNUuDBg0ynZMj1q1bp5deekmHDh0ynXILm82m3r17a9KkSapQoYLpnBzRqlUrbd26VdOmTdPw4cNN5wAAAAAA8jlWegGAC5sxY4YcDod69eqVZwdeJ06cUNu2bTV48GCXG3hJ0rVr1xQYGKiWLVvqyJEjpnNyhLe3t/r16yeJ1V55SWRkpMLCwuTt7a3evXubzsl2cXFx6t+/vzp27OiSAy/p99Wj33zzjerWravZs2ebzskRgYGBkn5flcx76QAAAAAApjH0AgAXlZSUlHGR1HlRMS9JS0vTRx99pHr16mnTpk2mc/7Wtm3b9OCDD+r999+X3W43nZPtnCs05s+fr+vXrxuuQXZwDjCfffZZFS5c2HBN9lqzZo3q1KmjBQsWmE65I1evXtXgwYPVoUMHnT171nROturWrZvKlCmjgwcPavv27aZzAAAAAAD5HEMvAHBRS5cuVUxMjBo2bKjGjRubzslWly9fVrt27fTKK68oMTHRdM4dS05O1rhx49SqVStduHDBdE62ql+/vlq0aKFr167pm2++MZ2DuxQXF6evv/5akjRs2DDDNdnH4XDonXfeUZcuXRQdHW06J9PWr1+vRo0aKTQ01HRKtvHw8NCQIUMk/fcelAAAAAAAmMLQCwBc1Oeffy7p91VeeeleUgcOHFDjxo21ZcsW0ylZtnPnTjVu3FiRkZGmU7KVc0Xh559/zjZlFjd//nwlJiaqXbt2qlGjhumcbJGQkKCnnnpKb7/9tqWPz/Pnz6tVq1aaO3eu6ZRsM3ToULm5uWn58uU6f/686RwAAAAAQD7G0AsAXFBUVJTCw8NVvHhx9erVy3ROtlmzZo0eeugh/fLLL6ZT7tpvv/2mFi1aaMWKFaZTss0TTzyhUqVKaf/+/XlqJUp+43A4Mlbc5JWtUS9fvqxHHnlEy5cvN52SLVJSUjRw4ECNHTvWdEq2qFChggICAmS32zVr1izTOQAAAACAfIyhFwC4oGXLlkmS+vbtm2fuxbNixQp169YtT90vKjExUU8//bQWLVpkOiVbeHp66plnnpH032MQ1hMVFaWjR4+qTJkyevzxx03n3LWLFy+qVatWeW5lpSRNmDBBY8aMMZ2RLZ577jlJ0uLFiw2XAAAAAADyM4ZeAOCCgoODJUndunUzXJI9goOD1bNnT9ntdtMp2S41NVX9+vXT0qVLTadkC+cxFxwcbOkt5PIz5/mja9eucnd3N1xzd2JiYtSuXTsdOnTIdEqOmTJlil599VXTGXetTZs28vb21qFDh3TixAnTOQAAAACAfIqhFwC4mJMnT+rgwYMqVqyYHnnkEdM5d2379u16+umn8+TAyyktLU19+vTRxo0bTafctaZNm6pEiRI6ceKEfvrpJ9M5yALn0CsgIMBwyd1JSUlR165ddeDAAdMpOW7SpEmaOnWq6Yy7UrBgQXXs2FGS9N133xmuAQAAAADkVwy9AMDFOC8WduzYUR4eHoZr7s6vv/6qHj16KDk52XRKjktNTdXTTz9t+RUObm5u6tKli6T/Dk9gHWfOnFFUVJQKFy6sNm3amM65K8OHD9eOHTtMZ+Sa0aNHa8OGDaYz7opz0Mq5AwAAAABgCkMvAHAxzouFVr8Xz40bN9S1a1ddvHjRdEquuXLligICAhQfH2865a44jz0uXFuPc2jevn17FSpUyHBN1k2dOlVz5swxnZGr0tLS1LNnT0sPzjt06CB3d3f9+OOPio2NNZ0DAAAAAMiHGHoBgAuJi4vTjz/+KDc3t4xtoqzq+eef1969e01n5LrDhw/rueeeM51xVx577DF5eHgoLCwsXw0t84K8sLVhZGSkxowZYzrDiCtXrqhnz55KTU01nZIl9957rx555BGlpaVp7dq1pnMAAAAAAPkQQy8AcCHr1q1TamqqHn74Yfn4+JjOybKNGzdq9uzZpjOMWbRokVavXm06I8vuuecetW3bVg6Hw9J/j/wmPj5emzZtks1mU6dOnUznZIndbteAAQMsO/TJDnv27NGkSZNMZ2SZc6Uo9/UCAAAAAJjA0AsAXIjzIqGVV2ncuHFDQ4cONZ1h3PDhwy29zaHzGOTCtXWEhIQoJSVFzZo1U6lSpUz1p2/fAAAgAElEQVTnZMn48eN14MAB0xnGvfvuuzp48KDpjCxxDr3WrFkju91uuAYAAAAAkN8w9AIAFxIaGipJatu2reGSrHvzzTd18uRJ0xnG/fbbb3r11VdNZ2RZu3btJP33mITr2759u6Tf7+dlRUeOHNHEiRNNZ7iElJQUy26TWqVKFVWtWlXXrl2z7OAOAAAAAGBdDL0AwEVcuXJFp06dkpeXl2rXrm06J0t2796tTz75xHSGy5g+fbp27NhhOiNLqlSpIm9vb50/f17R0dGmc3AH9uzZI0lq1KiR4ZKsGTt2bL7e1vB/7dy5U6tWrTKdkSXOY9B5TAIAAAAAkFsYegGAi3BeHGzQoIHc3d0N12TN2LFjlZaWZjrDZTgcDsuu9rLZbGrYsKGk34eZcG3p6emKioqSpIx/NyvZtWuXli9fbjrD5bz++uuWPKc6j0GGXgAAAACA3MbQCwBchPPioBUvWEvSsWPHFBISYjrD5Wzfvt2y9yjiwrV1HDt2TPHx8apQoYJKly5tOifTxo4dazrBJR0+fFhfffWV6YxMY6UXAAAAAMAUhl4A4CKsPvSaPn26HA6H6QyXNG3aNNMJWcLQyzqcq/GseP44ePCgNmzYYDrDZf3nP/8xnZBpvr6+kqR9+/YpJSXFcA0AAAAAID9h6AUALsLK9+NJTEzUvHnzTGe4rK+++krx8fGmMzLtj6s1GGi6NisPza06FM4tUVFRCg0NNZ2RKd7e3qpWrZpSUlJ06NAh0zkAAAAAgHyEoRcAuICYmBidOnVKXl5eqlmzpumcTFu0aJFiY2NNZ7is69eva8GCBaYzMq1y5coqXry4zp8/r+joaNM5+AtWHXrFx8dbcvu+3DZ9+nTTCZnGSlEAAAAAgAkMvQDABURGRkqSGjRoIHd3d8M1mcdKjb9nxYvWNptNfn5+krhw7crS09MVFRUlyXpDr+XLl1tyFWRuW7p0qRISEkxnZApDLwAAAACACQy9AMAFnDhxQpJUu3ZtwyWZt2vXLi5q3oFDhw5p69atpjMyrU6dOpL+e4zC9Vy4cEHx8fEqUaKESpcubTonU1atWmU6wRKSkpL0ww8/mM7IlLp160qSjh07ZrgEAAAAAJCfMPQCABdw7tw5SVK5cuUMl2Te8uXLTSdYxrJly0wnZFrZsmUl/fcYheux6vkjKSlJISEhpjMsIzg42HRCpjiPR84dAAAAAIDcxNALAFyA835JzgGDlYSGhppOsIywsDDTCZnmvHDNPb1cl1XPH5s2bbLcln0mrV69Wunp6aYz7pjzeOTcAQAAAADITQy9AMAFWHWlRmpqKlsbZsK+ffuUlJRkOiNTWOnl+qx6/ti2bZvpBEu5dOmSfv75Z9MZd+y+++5TwYIFFRcXp8TERNM5AAAAAIB8gqEXALgA50Vrq63UOHDggG7cuGE6wzLsdrsiIyNNZ2QKW5S5Pquu9GJgnnlWes0KFCigMmXKSOL8AQAAAADIPQy9AMAFWPWidXh4uOkEy7Haa8YWZa7Pqiu9rDTAcRVWe80YmgMAAAAAchtDLwAwLC0tTRcvXpSkjHfFW8WuXbtMJ1iO1V4zHx8feXh46OrVq2xR5qKsuFL09OnTunLliukMy7HaSlG2RwUAAAAA5DaGXgBg2MWLF5Wenq6SJUuqYMGCpnMy5dSpU6YTLMdqr5nNZuPCtYuz4krR06dPm06wJKu9bqwUBQAAAADkNoZeAGBYTEyMJKlEiRKGSzKPC5mZZ8XXrGTJkpKky5cvGy7B7TjPIc5/JytggJo158+fN52QKZw7AAAAAAC5jaEXABhmt9slSR4eHoZLMo8L15l3/vx5ORwO0xmZ4lyBmJqaargEt2PFc4gVh7+uICUlxVIDJOcxybkDAAAAAJBbGHoBgGHOi4FW29owMTFR8fHxpjMsx263KzY21nRGpri7u0viwrWrsuI5xHkfQ2TehQsXTCfcMQbmAAAAAIDcxtALAAxzrtJwDhasIjk52XSCZVnttXMem85jFa7FiucQq30PuBIrvXacOwAAAAAAuY2hFwAYlpaWJklyc3MzXJI5zm5kntVeO+exabXu/MKK5xCOpaxLT083nXDHOHcAAAAAAHIbQy8AMMz5TnirXRS00gV2V2O11855bFppJVF+YsVziNW+B1yJlV47zh0AAAAAgNzG0AsADLPq9k+FChUynWBZVnvtnPfj4cK1a7LiOcRq3wOuxEqvnRW33gQAAAAAWBtDLwAwzHkx0DlYsIpChQrJ29vbdIbleHp66t577zWdkSkMvVybFc8hpUuXNp1gWVZ67Th3AAAAAAByG0MvADCsYMGCkqTk5GTDJZlXtmxZ0wmWU6ZMGdMJmZaSkiLpv8cqXIuHh4cka51DypUrZzrBkgoVKiQfHx/TGXeMcwcAAAAAILcx9AIAw0qUKCFJunTpkuGSzOPCdeZZ8TW7ePGipP8eq3AtVjyHMDDPGqsNzZ3njpIlSxouAQAAAADkFwy9AMCwkiVLys3NTTExMZZaqSFJVapUMZ1gOZUrVzadkCnp6ek6f/68JAYVrsr57xIdHW245M498MADphMsyWqvm/OY5NwBAAAAAMgtDL0AwDA3N7eMe7Q4hwtW0aRJE9MJluPv7286IVNiYmJkt9t17733qlChQqZzcBvO1YPnzp0zXHLnKlSowOqfLPDz8zOdkCnOY9KKK1wBAAAAANbE0AsAXIDzXfBWumgtWW+A4wqs9po5j0lWarguK670kqSGDRuaTrAcq71mrPQCAAAAAOQ2hl4A4AKsuFJDkurUqaOiRYuazrAMT09PNWjQwHRGpjgvWrNSw3VZ9fzRqFEj0wmWY6XXLD09XRcuXJDE0AsAAAAAkHsYegGAC7DqSq8CBQpY6iKsab6+vvLw8DCdkSms9HJ9Vj1/tGzZ0nSCpZQtW1bVqlUznXHHLl++rNTUVPn4+MjT09N0DgAAAAAgn2DoBQAuwLlSw2rbk0lSs2bNTCdYhhVfK1Z6uT6rbm/YsmVLFStWzHSGZXTp0kU2m810xh1ja0MAAAAAgAkMvQDABVj1orUk9ezZ03SCZVjxteLCtetzDiTPnj1ruCRzChYsqPbt25vOsIyAgADTCZnCuQMAAAAAYAJDLwBwAc4tqw4ePGi4JPPq16+v5s2bm85weQ0bNpS/v7/pjExzHpNVq1Y1XII/U6pUKXl7eys2NtZyg/OuXbuaTrCEIkWKqF27dqYzMuXAgQOSpBo1ahguAQAAAADkJwy9AMAF+Pn5SZL2798vu91uuCbzAgMDTSe4vOHDh5tOyLT09HRFRUVJEvduc2E2m00NGzaUJO3Zs8dwTeY88cQTKl68uOkMl9e7d28VKlTIdEamOI9F57EJAAAAAEBuYOgFAC7A29tbVatWVXJysg4dOmQ6J9OefPJJlSpVynSGyypevLj69OljOiPTjh07pvj4eJUvX16lS5c2nYO/4Bws7N6923BJ5nh5eWnAgAGmM1yeFd9Y4DwWGZgDAAAAAHITQy8AcBFWXakhSR4eHho0aJDpDJc1YMAAeXl5mc7INFZqWIeVzx/Dhw+XzWYzneGy/P395evrazojU65cuaJTp07Jy8tLtWrVMp0DAAAAAMhHGHoBgItwvhveihetJWnYsGEqUIAfK//LZrNZcmtD6b/HIis1XJ+Vh17Vq1dX586dTWe4rNGjR5tOyLTIyEhJ0oMPPih3d3fDNQAAAACA/ISrkwDgIqy6PZlTxYoV1aVLF9MZLqdt27aqXr266YwscR6LrPRyfVWqVJG3t7fOnz+v6Oho0zmZNmHCBIbmt+Hn56ennnrKdEamsUoUAAAAAGAKVxcAwEX4+flJkvbv3y+73W64JmsmTpwoDw8P0xkuw93dXR9++KHpjCxJT09XVFSUJC5cW4HNZrP0aq969epZ8r53OW3ixImW3PqRoRcAAAAAwBSGXgDgIry9vVW1alUlJyfr4MGDpnOypHbt2nrttddMZ7iM0aNHW+5ePE5Hjx5VfHy8ypcvr9KlS5vOwR1wDhh27dpluCRrxo8fL09PT9MZLqNt27Z67LHHTGdkSUREhCS2RgUAAAAA5D6GXgDgQpo3by5JCgkJMVySda+//rpq165tOsO4qlWr6u233zadkWXOY/Chhx4yXII79fDDD0uS1q9fb7gkax544AG9++67pjNcQuHChfXFF1+YzsiSI0eO6JdffpGPj49q1aplOgcAAAAAkM8w9AIAF/L4449LkoKDgw2XZJ2Hh4dmz56dr+/PY7PZ9OWXX8rLy8t0SpY5j8GAgADDJbhTbdu2lZeXlyIiIix5Xy9JGjNmjPz9/U1nGDdhwgRVrVrVdEaWOM8dnTt3lru7u+EaAAAAAEB+k3+vSAKAC3rsscfk4eGhnTt36tKlS6Zzsqxp06YaOXKk6QxjhgwZolatWpnOyLKrV69qy5YtcnNzU6dOnUzn4A4VLlxY7dq1kyR9//33hmuyxs3NTXPnzlWhQoVMpxjz8MMP6/nnnzedkWUMzAEAAAAAJjH0AgAXUrRoUbVp00YOh0OrV682nXNXPvzww4ztGvOTRo0a6T//+Y/pjLuybt06paamqkWLFvLx8TGdg0xwDhqsvFq0Vq1amj59uukMI8qUKaNFixbJZrOZTsmSS5cuaefOnfLw8FD79u1N5wAAAAAA8iGGXgDgYpwXrb/77jvDJXfHw8NDQUFBuv/++02n5JqyZctq5cqVll+l4jz2WKlhPV26dJEkbdiwQQkJCYZrsm7AgAEaNWqU6Yxc5enpqRUrVqh8+fKmU7JszZo1Sk9PV+vWrVW0aFHTOQAAAACAfIihFwC4GOdF6/Xr1yspKclwzd0pVaqUVq1apSJFiphOyXFeXl4KCgqy9AVrSbLb7RmrDJ33mIN1lClTRv7+/kpOTtaGDRtM59yVjz76KF+tFpoxY4aaNm1qOuOuOAfmnDsAAAAAAKYw9AIAF3P//ffL19dXCQkJ2rx5s+mcu+br66vvvvtOXl5eplNyjIeHh1asWGH5C9aStGPHDsXFxalmzZqqVq2a6RxkgXPgYOUtDqXf7++1bNmyPPF99Xc++OAD9e/f33TGXUlKStK6deskMfQCAAAAAJjD0AsAXJBzW7kVK1YYLskerVu3VnBwsAoXLmw6Jdt5enoqKCgoz6xIcR5zbG1oXX+8r1dKSorhmrtzzz33aN26dfL39zedkmPGjx+vf/3rX6Yz7toPP/yghIQENWjQQBUrVjSdAwAAAADIpxh6AYALevrppyVJixYtUnx8vOGa7NGuXTutW7dO9913n+mUbFO8eHGtXr1anTt3Np2SLRITE7Vw4UJJ/z0GYT1169ZVvXr1dPnyZQUFBZnOuWve3t4KCQlRy5YtTadkK5vNpo8//ljjxo0znZItZsyYIUnq06eP4RIAAAAAQH7G0AsAXFDt2rXVsmVLXb9+XV999ZXpnGzz8MMPa9euXapTp47plLtWvXp1hYWFqW3btqZTss2SJUsUGxurJk2aqGHDhqZzkEU2m02BgYGSpGnTphmuyR5FixZVSEiIBg8ebDolW9xzzz0KCgrSmDFjTKdki5MnT2rt2rXy9PTUwIEDTecAAAAAAPIxhl4A4KL+eNHa4XAYrsk+lStXVmhoqLp27Wo6Jcs6dOigXbt2qUaNGqZTspVzQOI89mBdffv2VdGiRbVt2zYdOHDAdE62KFiwoL788kt9+umncnd3N52TZZUqVVJoaKi6detmOiXbzJgxQw6HQ7169cpTq3kBAAAAANbD0AsAXFS3bt1UpkwZHTx4UNu3bzedk62KFi2qlStXavbs2SpevLjpnDtWrFgxTZ8+XWvWrJG3t7fpnGwVERGhiIgI+fj4sLVhHlC0aFE988wzkqTp06cbrsle//znP7V161ZVq1bNdEqm9e7dW7t371bdunVNp2SbpKQkzZ49WxIDcwAAAACAeQy9AMBFeXh4aMiQIZKkzz//3HBNzhg4cKB++ukn9ejRw3TK3woICNDhw4c1bNgw2Ww20znZzrnKa+DAgfLy8jJcg+wwfPhwSdLChQt17do1wzXZ66GHHtK+ffs0atQoFSjg+v9ztnTp0lqxYoW++eYb+fj4mM7JVkuXLlVMTIwaNmyoxo0bm84BAAAAAORzrn+VAADysaFDh8rNzU3Lly/X+fPnTefkiDJlymjZsmVasWKFypUrZzrnFqVKldKSJUu0atUqlS9f3nROjoiJidHixYslScOGDTNcg+xSp06dPHlvQCcvLy9NmTJFO3bsUPPmzU3n3JaHh4dGjhypw4cP56ntDP/oj9ui5sU3BAAAAAAArIWhFwC4sAoVKiggIECpqamaNWuW6Zwc1a1bNx07dkxffvmlfH19Teeofv36mj59uk6cOJHnt/ubN2+ekpKS1KFDB1WpUsV0DrLRiBEjJP2+WjQv3Rvwj5o2bart27crODjYZbYNLFCggPr27auff/5ZU6dOzXOru5wiIyMVFham4sWLq1evXqZzAAAAAACQzZFXr4AAQB6xYcMGPfrooypVqpROnDihe+65x3RSrggLC9O0adP07bffKjk5OVee08PDQz169FBgYKBatGiRK89pWmJioqpXr64zZ85o1apVCggIMJ2EbGS32/XAAw8oOjpay5Yts8RWoncjPT1d3333naZNm6aQkJBcH/R5e3vrmWeeUWBgoGrWrJmrz21Ct27dtGrVKo0ePVqTJ082nQMAAAAAAEMvAHB1DodDzZo1U3h4uN5991298cYbppNy1eXLlzV79mzNnDlTJ0+ezJHn+Mc//qEhQ4Zo8ODBKl26dI48h6uaPHmyXnrpJT344IOKjIy0xP2RkDnTp09XYGCgatSooYMHD8rd3d10Uq44fvy4Zs6cqaCgIJ04cSLHnsfd3V0tWrRQ79691bdvXxUpUiTHnsuV7Ny5U82bN1fhwoV14sQJlSlTxnQSAAAAAAAMvQDACrZs2aLWrVuraNGiOnnypEqUKGE6yYjTp08rPDxc4eHhCgsLU2RkpJKSkjL1GJ6envL19VXTpk3l7+8vf39/VapUKYeKXdvVq1dVuXJlXblyRWvWrFHHjh1NJyEH2O121apVSydOnNCXX36pwYMHm07KdT/99JOCg4MVEhKiPXv2KC4u7q4er1KlSmratKk6d+6sTp066d57782mUmtwOBxq2bKltm3bpnHjxmn8+PGmkwAAAAAAkMTQCwAso2PHjlq3bp1GjRqlKVOmmM5xCXa7XUePHlV0dHTGr7i4OKWmpkr6fQWGt7e3ypUrl/GrevXq8vDwMFzuGsaOHasJEyaoZcuW2rx5s2w2m+kk5JDFixerd+/eKl++vI4dOyYvLy/TScY4HA6dPHlSu3fv1tGjR3Xu3DlFR0fr3Llzun79ulJTU2Wz2VSwYEHdd999KleunMqWLavy5curfv368vPzy7P36LpTa9asUefOnXXffffpxIkT8vb2Np0EAAAAAIAkhl4AYBl79+6Vr6+vPDw8dPToUf3jH/8wnQQLO3funKpWraobN24oNDRUTZs2NZ2EHJSenq5GjRopKipKkyZN0iuvvGI6CRaVnp4uX19f7d+/X5MnT9bo0aNNJwEAAAAAkIEbdwCARTRo0EC9e/dWSkqK3nrrLdM5sLjx48frxo0b6tatGwOvfKBAgQKaOHGiJGnixImKjY01XASrWrRokfbv36/7779fgYGBpnMAAAAAALgJK70AwEJOnDihmjVrKi0tTfv371fdunVNJ8GCjh8/rlq1aik9PV0HDhxQ7dq1TSchFzgcDrVt21abN2/Wq6++mjEEA+5USkqKatasqVOnTmnu3LkaMGCA6SQAAAAAAG7CSi8AsJAqVaroueeek8Ph0KhRo8T7FpBZDodDY8aMUWpqqvr378/AKx+x2WwZg64pU6bo8OHDhotgNRMnTtSpU6dUu3Zt9evXz3QOAAAAAAC3YKUXAFjMxYsXVatWLV25ckUzZ87UkCFDTCfBQr7++mv93//9n4oVK6ZDhw6pQoUKppOQywYPHqzZs2erSZMm2rFjh9zd3U0nwQL27dunRo0aKTU1VZs3b1arVq1MJwEAAAAAcAtWegGAxZQqVUqfffaZJGnMmDH69ddfDRfBKs6dO6eRI0dK+n2lDwOv/Gny5MmqUKGCdu3apcmTJ5vOgQXY7XYNGDBAqamp+uc//8nACwAAAADgshh6AYAF9erVS927d1d8fLwGDx7MNof4Ww6HQ8OGDVNsbKw6dOiggQMHmk6CId7e3po1a5Yk6c0332SbQ/ytCRMmaO/evapUqRL3ggMAAAAAuDS2NwQAi7pw4YJq167NNoe4I2xriP/l3OawcePG2rlzJ9sc4rb27t2rxo0bs60hAAAAAMASWOkFABZVunRptjnEHfnjtob//ve/GXhB0n+3OYyIiGCbQ9wW2xoCAAAAAKyGoRcAWBjbHOLv/O+2hs8++6zpJLiI/93m8ODBg4aL4Gref/997du3j20NAQAAAACWwdALACzMZrNp+vTp8vHxUUhIiCZNmmQ6CS7m008/VXBwsIoVK6Yvv/xSNpvNdBJcSPv27TV48GClpKSoe/fuio2NNZ0EF7F69Wq9++67kqQ5c+bonnvuMVwEAAAAAMDfY+gFABZXunRpzZ07V5L0+uuv6/vvvzdcBFcREhKiUaNGSZJmzpzJtoa4rU8++UQNGjTQ8ePH1bNnT6WmpppOgmE//fSTevfuLYfDofHjx7OtIQAAAADAMhh6AUAeEBAQoPfee08Oh0N9+vTR4cOHTSfBMOcAIz09XWPHjlXPnj1NJ8FFFSlSRKtWrVLJkiUVEhKil19+2XQSDIqNjVVAQIDi4+P11FNPaezYsaaTAAAAAAC4YzYHN4ABgDzB4XCod+/eWrJkiapUqaJdu3bJx8fHdBYMuHbtmpo2baqffvpJXbt2VVBQkAoU4H0u+Gvbt29XmzZtZLfbNWfOHO7/lg+lpqaqU6dOCgkJka+vr7Zt26YiRYqYzgIAAAAA4I5xBQwA8gibzaY5c+bI19dXJ06c0NNPP802ZflQWlqa+vTpo59++kl16tTRwoULGXjhjrRo0ULTp0+XJA0bNkyhoaGGi5DbXn75ZYWEhKhUqVJauXIlAy8AAAAAgOVwFQwA8pDChQtr5cqVKlWqlDZu3KgxY8aYTkIuGzdunFavXi0fHx+tWrVKRYsWNZ0ECxk0aJBGjhyplJQUde/eXWfOnDGdhFwyd+5cffLJJypYsKCCgoJUsWJF00kAAAAAAGQa2xsCQB60Y8cOtW7dWna7XZ999plGjBhhOgm5YO7cuRo4cKDc3Nz0ww8/qE2bNqaTYEGpqanq0KGDNm7cqDp16mjLli0qUaKE6SzkoNWrV6t79+6y2+2aNWuWBg0aZDoJAAAAAIAsYaUXAORBzZs314wZMyRJ//znPzV37lzDRchpixYt0uDBgyVJU6dOZeCFLHN3d9e3336r2rVr69ChQ3r00UcVGxtrOgs5ZMOGDerRo4fsdrtefvllBl4AAAAAAEtj6AUAedSzzz6rjz/+WNLvW5YtWrTIcBFySlBQkPr166f09HSNHz9egYGBppNgcT4+Ptq4caOqVaumvXv3qkOHDrp27ZrpLGSzH3/8UQEBAUpOTtaIESM0adIk00kAAAAAANwVhl4AkIeNGTNG7733nhwOh/r166dvv/3WdBKyWXBwsHr16qW0tDSNHTtW48aNM52EPKJMmTLatGmTKlWqpF27dumxxx5TXFyc6Sxkk82bN6tTp05KTEzUoEGDNHXqVNlsNtNZAAAAAADcFYZeAJDHOQchaWlp6t27txYuXGg6Cdlk6dKlGduSjRkzRuPHjzedhDymQoUK2rRpkx544AGFh4erbdu2iomJMZ2Fu7R+/Xp16tRJCQkJeuaZZzRjxgwVKMD/LQAAAAAAWB//7xYA8oF3331X77zzjtLT09W/f399+eWXppNwlxYuXKhevXopNTVV//rXv/TRRx+xSgM54oEHHtDWrVtVtWpVRUZGqlWrVjp//rzpLGRRcHCwAgIClJSUpCFDhmju3Llyc3MznQUAAAAAQLZg6AUA+YDNZtObb76pDz74QA6HQ0OHDtX7778vh8NhOg2Z5HA4NHnyZPXv31/p6el6++23NXHiRAZeyFEVK1bU1q1bVbNmTR08eFBNmjTR3r17TWchExwOh6ZMmaLu3bsrJSVFI0eOZIUXAAAAACDPsTm44gkA+crUqVP14osvyuFwqGfPnpozZ44KFy5sOgt3ICkpSc8995wWLFggSZo0aZJeeeUVw1XITy5evKiuXbsqLCxMXl5emj9/vp566inTWfgbSUlJGjZsmObPny9Jeuutt/TWW28xLAcAAAAA5DkMvQAgH/ruu+/Ut29fxcfHy8/PTytXrtT9999vOgt/4dy5c+revbvCw8NVuHBhLViwQD169DCdhXwoKSlJw4cP17x58yRJb7zxht5++21WDLmoc+fO6YknnlBYWJgKFy6sefPmMagEAAAAAORZDL0AIJ86fPiwAgICdOLECZUuXVpBQUF66KGHTGfhNiIiItStWzdFR0erYsWKCg4O1oMPPmg6C/mYw+HQJ598opdeeknp6enq1q2bFixYoKJFi5pOwx9ERESoe/fuOnv2rCpWrKhVq1apQYMGprMAAAAAAMgxvCUXAPKp2rVra9euXWrbtq0uXLigVq1aac6cOaaz8D++/vprPfzww4qOjtbDDz+siIgIBl4wzmazadSoUVq7dq2KFy+ulStX6qGHHtKpU6dMp+H/++abb/TII4/o7NmzatGihSIiIhh4AQAAAADyPIZeAJCP+fj4aN26dXr++edlt9s1aNAgjRgxQgkJCabT8r3ExESNHj1a//d//6fk5GQNHTpUGzZsUKlSpUynARkee+wxhYeHq8jMfIEAACAASURBVEaNGjp48KB8fX01b948sZGAOXFxcRo0aJD69u2rpKQkDRkyRBs3buTcAQAAAADIF9jeEAAgSZo1a5YCAwNlt9tVpUoVzZ07Vw8//LDprHwpLCxMAwYM0JEjR+Tm5qapU6dq+PDhstlsptOA27p69ar69++vVatWSZI6d+6sGTNmqHz58obL8pe1a9dqyJAhOnv2rDw9PTV58mQFBgZy7gAAAAAA5Bus9AIASJIGDx6s8PBw1atXTydOnFDLli31wgsvsOorFyUmJurll19W8+bNdeTIEdWqVUs7duzgojVcnre3t1asWKH58+erePHiWr16terUqaP58+ez6isXOFd3derUSWfPnpW/v7+ioqI0YsQIzh0AAAAAgHyFlV4AgJukpKTovffe04QJE5SWlsaqr1zyx9VdBQoU0Msvv6y3335bhQoVMp0GZEp0dLSGDh2q1atXS2LVV07739Vd48eP1+jRo+Xm5mY6DQAAAACAXMfQCwBwW5GRkRowYIAOHDggm82mkSNHasKECSpSpIjptDwlMTFRb775pqZMmaL09HTVqlVLc+fOlb+/v+k0IMscDocWLlyoF154QXFxcfL29takSZM0aNAgubu7m87LE86dO6fXX39d8+bNkyT5+/tr7ty5qlWrltkwAAAAAAAMYntDAMBt+fn5affu3XrjjTdUoEABTZ06VdWrV9fMmTOVmppqOs/y0tLSNHfuXNWoUUMff/yxJOlf//qXIiMjGXjB8mw2m5555hkdOnRInTt31tWrVzVs2DDVqVNHy5YtY8vDuxAXF6fXX39dVapU0bx58+Tp6akPP/xQO3bsYOAFAAAAAMj3WOkFAPhbkZGRGjp0qPbs2SNJql69ut5//3316NGD+8VkksPh0KpVqzR27FgdPnxYklS/fn3NnDmTYRfyJIfDoaVLl2rs2LE6fvy4JKlRo0b64IMP1LZtW8N11pGYmKjPPvtMEydOVGxsrCSpe/fumjhxomrUqGG4DgAAAAAA18BKLwDA3/Lz89OuXbu0ePFiVa1aVUePHtVTTz2lJk2aaOPGjabzLOPHH39U8+bN1b17dx0+fFgPPPCAFi5cqKioKAZeyLNsNpuefvppHT58WNOnT1eZMmW0e/dutWvXTo8++mjGMB23l5qaqlmzZqlatWp65ZVXFBsbq1atWiksLExBQUEMvAAAAAAA+ANWegEAMsVut2v27Nl65513dP78eUlSu3bt9N577zG4+RN79uzRG2+8obVr10qSSpYsqTfeeEPPPfecPDw8DNcBuSshIUFTp07VpEmTdPXqVUm/r1gaOXKkWrVqxerR/+/69ev6+uuv9e9//1tHjhyRJDVo0EATJ05U+/bteZ0AAAAAALgNhl4AgCy53YXrxo0bKzAwUD179pSXl5fhQrOSkpK0bNkyTZs2TaGhoZKke+65Ry+99JJGjx6tokWLGi4EzIqJidGkSZM0depUJScnS5Jq1aql4cOH65lnnpG3t7fhQjOcK+Lmz5+v+Ph4SVLlypX13nvvqWfPnipQgI0aAAAAAAD4Mwy9AAB3JSYmRh999JFmzpyZcZ+Ze++9VwMHDtSwYcNUtWpVw4W569SpU5oxY4Zmz56ty5cvS5KKFSumwYMH69VXX1XJkiUNFwKuJTo6WjNnztTMmTN17tw5SVKRIkXUt29fBQYG6sEHHzRcmPPsdrtWrlypadOmacuWLRkfb9GihQIDA9WjRw9WhQIAAAAAcAcYegEAskViYqKWLFmiadOmKSIiIuPj7du3V2BgoDp27KiCBQsaLMw5qamp+uGHHzRt2jStWbNGzh+tDRo00IgRI9S7d28VKVLEcCXg2ux2u1atWqVp06Zp8+bNGR9/6KGH1KdPHz3++OOqWLGiwcLslZaWpvDwcAUHB2vBggU3Dfz69eun4cOHq379+oYrAQAAAACwFoZeAIBsFxERoenTp2vRokVKSkqSJHl7e6tTp04KCAhQhw4dVLx4ccOVd+fatWtav369goODtXr16oxVbh4eHurZs6cCAwPl7+/PfXeALDh8+LC++OILzZ8/X9euXcv4+IMPPqiAgAAFBATIz8/Pclv9JSQkKCQkRMHBwfr+++916dKljM/Vrl1bgYGB6tevn4oVK2awEgAAAAAA62LoBQDIMVeuXNG8efM0e/ZsHT58OOPj7u7ueuSRRxQQEKDHH39clStXNlh5506fPq3vvvtOwcHB2rJli+x2e8bnatSooYEDB+rZZ59lC0Mgm1y/fl1BQUEKDg7WunXrlJCQkPG5cuXK6fHHH1eXLl3UtGlTlShRwmDp7aWnp+v48ePasmWLgoODtWHDhoz7l0lSpUqVFBAQoCeeeEIPP/wwQ3IAAAAAAO4SQy8AQK44fvx4xsBo27ZtSktLy/hc7dq19dBDD6lhw4Zq1KiR6tWrJ09PT4O1UkpKig4ePKg9e/Zo9+7dCg0N1YEDBzI+X6BAATVv3jxjcFejRg2DtUDel5SUlDE8Cg4O1tmzZ2/6fMWKFdWoUSM1bNgw41duDsKcA67du3drz5492rNnjyIjIxUfH5/xNTabTf7+/hnnjTp16jDoAgAAAAAgGzH0AgDkuitXrmjt2rUKDg7W2rVrb7ooLEkFCxZU3bp1M4Zgfn5+euCBB3Tfffdl+3ZmDodDMTExOn36tCIjIzMuWB84cEApKSk3fW2RIkXUoUMHBQQEqFOnTi65sgTIDxwOh/bu3avg4GCFhIQoKipKN27cuOXrKlasKF9fX1WsWFHlypVT2bJlb/rPe++9946HTna7XRcuXNC5c+d07tw5RUdHZ/znsWPHbhlwOZUrV07+/v7q3LmzOnfurDJlytz13x8AAAAAANweQy8AgFEpKSkKDw+/aXXEkSNHdLsfTwULFlSZMmVuuXBdtmxZFS1aVO7u7hm/JCk1NTXj1/Xr12+5UO28eP3HbQr/qHr16jetGmnatKkKFSqUo68HgMxLS0vTkSNHMlZm7tmz508HYX/k6empMmXKqHDhwjedPxwOh+x2e8b5IzY2VpcuXbrteemPypUrd8tqM4ZcAAAAAADkHoZeAACXEx8fr6ioqIwh2L59+3T27FnFxsbmyPMVL15c5cuXV/369TNWl/n6+qpYsWI58nwAcp5zEHbgwAFFR0ffduB99erVO348m82m0qVL3zRsd/6+YsWK8vPzY8AFAAAAAIBhDL0AAJaRmJio8+fP33LhOjo6Wjd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YqBPwAoDRo0cjMTFR/J2npaUZfT7+2DiWcCzhWEJERERERGQYg15EnYh8Z1N+fr7BdkuWLEF6ejoKCgrw8ssv622jUCgwffp0g+eQgl6AZvWyXGpqqghU/fa3vzVaT0KhUCAkJASApp6FlDoR0ATPpH8PGTIEs2bNMngeAHj11VfFcVpamsF2/fv3F4E/Q+eRJseysrLESvG2sLCw0JrYe1xKSoo4Xrp0qdH6Zr6+vvDz8wMAXL9+HUVFRQbbrlq1CnZ2dnpfGzBggAj8qdVqnDp1yuB5AgMDMWjQIL2vlZeXi4Crvb290foo3bp1E+m/AM3vipy8DytXrjR4HldXV50aMM9KZmamOF69erXBdoMHD0ZwcDDGjRuHuXPn4v79+wCA48ePi+OQkBCD9xEAevfujfDwcACaoKu+enhEndnkyZPFsZSeUCIPhAUHB4vFFI/XrQJ+CHrZ29vDx8cHAHDs2DER2AkLCzOaBtfd3V3szlKr1To1IH8qHEs0OJZwLCEiIiIiIjKEQS+iTmTcuHHiODExEevWrUNWVpbO7ik7Ozu4urrqrHqW8/X1NfpZ8jRCUu0vybfffiuOR44cabLf8s+Sr+yXn0ffavzHjR49WlyTvh0CkpkzZxpMfQgAjo6O8Pb2BqC5NmO74czl5uaGPn36GHz9Se+ZNIkLGL5WCwsLk4FCedovKRWXPvpSYEq++eYbcTx58mSjk6wA4O3tDWdnZwCaWjE3b94EoJnQlnZs9OzZU+v3WR95DZhn5datWygtLQWgCdAamyQHgG3btuHAgQOIjo4WOyLk3+2IESNMfqahvweirmDChAniGfJ40KugoECMZ35+fvDy8gKgqcP18OFD0a6+vh5XrlwBoEktKD3v5cExeTpAQ+TPzwsXLjzN5bQ7jiU/4FhiHMcSIiIiIiLqqljTi6gT8fT0xLx58/Cvf/0LgGZV+7Fjx2BlZQVfX19MmjQJgYGBWmmhDJHS4hgiDxqpVCqt165fvy6Oja3U1ke+a0x+nvT0dHh4eJh9nnv37qGpqUlv8fZRo0aZfP+wYcPEBNPjO9mexnPPPWfwtdbWVjEZBkDsfDOXof4NGjRIb1orOXlqpDt37hhsZ6z/t27dEsfmfkeenp4i3VJFRQVcXV1RW1sr0ji5uLgYDUwCmp0Yz5o8JZQ5fzf6yH+Po6OjER0dbfZ72+N3j6gj6dWrF8aOHYucnBxcvnwZDQ0NIsgjpTa0traGl5cXfH19kZGRgZaWFuTn52P8+PEANLu8pB1dUmpD4MmfVfI2FRUVbb+4dsCxRBvHEvNwLCEiIiIioq6EQS+iTmbz5s2wt7fHwYMH8ejRIwCaVc/nzp3DuXPnsHXrVri6umL+/PlYvHgxrK2t9Z5Hqs/xNBoaGtrlvW05j/R+fUEvBwcHk++Vp3Gqq6sDoJnIO3bsmNH3/fKXv9RKzyUxluKxPa5TH1OBSwBaOwZqa2vNave4e/fuiWND6a8eJ28n9V/++cbul8Sc77GtpO8eMH4PjGmvvweirmLy5MnIycmBSqVCbm6uSLUrBb2ef/55WFlZae3gOX/+vAh6SWlxe/TogUmTJok2T/qskreR0sq1B44l+nEsMY5jCRERERERkXkY9CLqZKysrBAZGYlly5bh6NGjyMzMxMWLF0UADABu3ryJbdu24fDhwzhw4IBID9RepHpeALB27donKhIv1WgBoNXnadOmYcyYMU/UD0OBO2NpHfV9ttS+vLwcH330kdH3hYaG6p2o7NHD8ONWfr+sra2xZs0ak/2T01fI3tRnSuS79IzdF2Mr5aUdFYAmDZY55PdXeo+575WY8z2aS34NcvLv5mnJz/G73/1OpKoyhzkTtkSdTWBgIGJiYgBoUhxOnz4dTU1NItWsFOzy9PSEnZ0d7t+/L9LZqdVqkV7P19f3qQMMgPbz8UmfT8ZwLNGPY4lxHEuIiIiIiIjMw6AXUSc1YMAAREREICIiAo2NjTh//jxycnKQmZmJ27dvA9BMvEVGRmLv3r3t+tl2dnZipfXs2bPh6ur6VOeRT1YOGzYMy5cvb5f+PXjwwGQb+apoef2yZ0G+Ur25uRmLFy82WcvEHI2NjSbbyHcvPMkEmpw87ZV8pb4x8nbSZJz8Ppuzq8Kc71GiVquNToQ2Nzfr/bl8ovBpV8rLv9+AgAAEBAQ81XmIugoXFxcMGTIEZWVl+PrrrwFoahK1tLQA0NTzAoBu3bph7NixOHnyJAoKCqBUKlFcXCx21chTGwKaMaWmpgaA5hljageT/DnVluDZj4VjCccSIiIiIiIiYtCLqEuwtbVFUFAQgoKCEBkZiYMHD+KDDz4AoEkDVVtb266BHYVCIYJehYWFJoNeTU1NsLCw0Em1qFAoxHFhYaFZn3337l2TqYpu3Lhh8jzFxcXiePDgwQA0E63Xrl0zqx9PomfPnnByckJNTQ3UajUKCwtN7mprbGyElZWV0QnNsrIytLa2Gl2lL79O+f1+EvL3mXt/5O2k9/ft2xdWVlZQKpUoLS2FUqk0en0lJSVGP0M+MalUKg3uOGxpaTE4wSq/tps3bxr9PAA4ffo00tPTMXjwYAQFBcHT0xMKhULsUCksLDQ5UalUKtHS0tKmFKNEHd2UKVPw97//HSUlJaiqqkJubi6AH+p5Sfz8/HDy5Ek0Nzfj4sWLohYjoNkxwgnsmQAADFdJREFUJjd48GAR9Lp27ZpIh2jI1atXxfHTPh/14ViiH8eSH3AsISIiIiIienrdfuoOEFH7iYyMxPz58zF+/HiDq40tLCywaNEiuLm5iZ9VVVW1az98fX3Fsam6JQCwdetWeHt7IyAgAPv37xc/l0/WnT9/HtXV1UbPc+nSJfj7+8PHxwfh4eEG2505c8boeWpqakSQzdnZGcOHDzd5DW0lv1Zz7tlbb70Fb29vBAYG4ssvv9Tbprm5WUwUG5KZmSmOp06damZvtcn7/tVXX0GpVBptn5eXJyae7e3t4eLiAkCTYmrixIkANJOHp0+fNnoeU9+jvJ5bfX29wXZXrlzRSpEl5+rqKnYflJWVoaKiwuhnHj9+HEeOHEFsbKyY2HzSv4cDBw7A19cX/v7+2LJli8n2RJ3RlClTxPHZs2dx4cIFAICPj49WAEPa9QUAubm54rkwZMgQDB06VOuc8mfV8ePHTfZB3ub5559/wiv4aXAs4Vgi4VhCRERERERdFYNeRJ1ISUkJLl26hPr6epw4ccJgO5VKpbUauX///u3ajxdffFEcZ2Vl4ezZswbblpWVISUlBYAm2DRq1Cjx2rBhwzBy5EgAmomrbdu2GTyPWq3Gjh07AAD/+9//xOSXPnl5eTh37pzB13ft2iUmrmbNmtWutVwM+fWvfy2O//GPf+D777832Pbbb79FVlYWVCoVqqurMXr0aINtP/nkE4M1RioqKpCamgpAUwtu2rRpT9X3oUOHip0X9+7dQ0JCgsG2KpVK63sMDg7Wur+zZs0Sxzt37jQ46VlXV4dDhw4Z7Ve/fv3EsZQiTZ89e/YYPc+cOXMAaH7HjF1bfX090tPTAWh2o0iTrrNnzxY7A4qLi8Xvuz737t0T6Ubv3r0LT09Po30j6qzGjRuHXr16AdDsevnuu+/Ez+Xc3d3F7l4pzSGgm9oQ0IxN0vPmyJEjRnf9FhcX45///Kf4d3BwcBuu5sfDsUSDYwnHEiIiIiIi6roY9CLqRMLCwsRxVFSUmPyTU6vV2Lp1q6h54uvr2+41qzw8PMSEk1qtxpo1a/SupC4rK8Pvf/97sSvNz88Pv/rVr7TarFmzRhx/8cUX+NOf/qQzedXc3IzNmzcjJycHgGaV94oVK4z28c033xSTqBKVSoXdu3cjKSkJgGbl+KuvvmrOJbdZYGCgmOxrbm5GRESE3pSOhYWFeOONN8S/X3zxRTz33HMGz5ubm4uNGzfq3LPy8nK88soraGpqAgCsXLnSZH0bY1avXi2OP/74Y+zfv19ngrSxsRFvvvmm2LHh4OCA1157TavN3LlzReDz2rVrWLt2rU49merqaqxcudJkzRd56rL4+HhRy07S0tKCLVu2GA0QA8DSpUvFSv/Dhw8jISEBKpVKq839+/e1+vrSSy+JGkCOjo5YtGiRaBsVFaU1mS6pqanB6tWrxd+mi4sLQkJCjPaNqLOysrKCv78/ACAjI0OME48HvSwsLMTPioqKxIKFx1MbApqFFNLY9PDhQ7zyyisoKirSaXf58mVERESI5+a8efO0Uir+nHEs0eBYwrGEiIiIiIi6Ltb0IupEwsLCkJycjMLCQjQ0NGDBggWYMGECPDw84OjoiLq6OuTk5IiV3z179sS77777TPoSHR2Nq1evorS0FA0NDYiIiMCYMWMwduxY9OjRA99//z1OnTqF1tZWAJrJnJiYGJ3zBAYGYtmyZdi3bx8A4NNPP0VGRgamTp2Kfv36obKyEllZWaisrBTvee+99zBkyBCDfbO0tERdXR0WLlyIwMBAjBw5Ek1NTcjKyhL3pkePHoiKijJZH6y9WFhYYMeOHViwYAHq6+tRWVmJBQsWYOLEifDy8sKjR49QVFSEr776SkwAuri44L333jN6XktLSyQnJyM7OxszZsyAvb09rl+/jhMnTohJZG9vb5NBQlOmTJmC5cuXY+/evVCpVIiJiUFycjKmTJkCe3t73L59GydPnhSpoSwtLfHhhx9qraAHgO7du2Pz5s1YvHgxGhoakJmZiZkzZ+KFF15A//79UVZWhuPHj6OxsREuLi4oLS012Kf58+fjr3/9Kx48eIDKykqEhIRg9uzZUCgUqKmpwcmTJ1FdXY3evXvDy8tLBE0fp1AosHHjRmzYsAFqtRrbt29HamoqAgMDxbVlZGSIiVM3NzetyWQAeOONN5Cfn4+8vDwolUqsX78e+/fvx8SJE2FjY4OysjKcOHFCTBzb2Nhg27ZtRmvoEHV2gYGByMzMFIGsx+t5Sfz8/JCRkSH+bWtrq7OAQhIdHY2ioiKUlpbi9u3bmD9/PgICAsQup0uXLuHMmTPiM4cOHYqNGze296U9MxxLNDiWcCwhIiIiIqKui/8DIupEunfvjoSEBLz22mu4ePEiAE0qHn3peJycnBATEwNvb+9n0hc7OzskJSXh7bffRnZ2NgDgwoULYmW2nLu7O+Li4jBo0CC951q/fj1+8YtfID4+Hs3NzaisrMRnn32m087GxgYbNmzAb37zG6N9W7x4MYqKinD27FmcOnUKp06d0nq9d+/eiI2NNVkkvr0pFAocPnwYa9euxeXLl6FSqXDmzBm9u+TGjBmDuLg49O7d2+g5d+zYgXfeeQe3b9/WqpcmeeGFF/DRRx9p1ch5Wu+88w4cHR0RFxcHpVKJkpISlJSU6LQbOHAgYmNjDf7ueXp64uDBg1ixYgXu3LmDuro6ne9boVAgNjZWK5XX4xwdHbFr1y68/vrraGhoQFNTE44cOaLVxsnJCbGxscjIyDA4UQkAoaGhsLS0xMaNG9HY2Gjw2nx8fBAXFwdbW1utn1tZWWHfvn344x//iLS0NACaXSn6dpkMHDgQ27dvN5pqjKgrkNf1AnTreUmkHWGSgIAAWFpa6j1nnz59kJSUhLfeegs5OTl49OgRTp8+rbfu06xZs7B582adv+efO44lGhxLOJYQEREREVHXxKAXUSfj6OiIQ4cOISMjA19++SWuXLmC2tpatLa2wtHREW5ubpg6dSpCQ0Of+USeg4MD9u7di6+//hppaWnIy8tDdXU1Wlpa4ODggBEjRmD27NkICQkxOEEpiYiIwJw5c5CcnIycnByUl5fjwYMHsLGxgYuLCwICArBw4UI4Ozub7FevXr2wb98+pKSk4PPPP8f169fR2toKFxcXTJs2DYsWLWpTeqa2UCgU+Pzzz3HixAmkp6ejoKAAdXV1ePToEfr27YvRo0dj3rx5mDZtmlm1xmbOnAl3d3fs2bMHOTk5qK2thZ2dHXx8fPDSSy/pTCq3VUREBEJCQpCUlITs7GzcunULDx48QJ8+fTB8+HDMmDEDYWFhsLa2Nnoed3d3HD16FIcOHUJ6ejpu3LgBtVqNQYMGYebMmVi+fLlOWih9/P39kZGRgU8//RT/+c9/UFFRAQsLCygUCsyYMQPh4eFwcHDQ2iViyJw5czBhwgR89tlnOHPmDEpLS9HY2Ag7OzuMGjUKc+fORXBwMLp37673/dKK+yVLliA1NRW5ubmoqqrCw4cP0adPH3h4eGD69OkIDQ0VtYyIujJnZ2d4eHjg2rVrAHRTG0rc3Nzg5OSEmpoaAPrreck5ODhg3759yM7ORlpaGi5cuCDGyQEDBmDMmDEIDQ2Fn59f+17Qj4hjiQbHEo4lRERERETU9VioDVWlJiLqRI4cOYINGzYA0NQMef3113/iHj07U6dOFTVHpMliIiKiJ8GxhIiIiIiIiDqibj91B4iIiIiIiIiIiIiIiIjaikEvIiIiIiIiIiIiIiIi6vAY9CIiIiIiIiIiIiIiIqIOj0EvIiIiIiIiIiIiIiIi6vAY9CIiIiIiIiIiIiIiIqIOj0EvIiIiIiIiIiIiIiIi6vAs1Gq1+qfuBBEREREREREREREREVFbcKcXERERERERERERERERdXgMehEREREREREREREREVGHx6AXERERERERERERERERdXgMehEREREREREREREREVGHx6AXERERERERERERERERdXgMehEREREREREREREREVGHx6AXERERERERERERERERdXgMehEREREREREREREREVGHx6AXERERERERERERERERdXgMehEREREREREREREREVGHx6AXERERERERERERERERdXgMehEREREREREREREREVGHx6AXERERERERERERERERdXgMehEREREREREREREREVGH939EpCDER6nNxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import itertools\n",
    "from math import pi\n",
    "\n",
    "\n",
    "def normalize(x, xmin, xmax):\n",
    "    return (x-xmin)/(xmax-xmin)\n",
    "\n",
    "\n",
    "def plot_circles(ax, locs, names, max_s, stats, smax, smin, fc, ec, lw, \n",
    "                 zorder):\n",
    "    s = np.asarray([stats[name] for name in names])\n",
    "    s = 0.01 + max_s * np.sqrt(normalize(s, smin, smax))\n",
    "    \n",
    "    fill = True\n",
    "    for loc, name, si in zip(locs, names, s):\n",
    "        if fc=='w':\n",
    "            fill=False\n",
    "        else:\n",
    "            ec='none'\n",
    "            \n",
    "        x = np.cos(loc)\n",
    "        y = np.sin(loc)\n",
    "        \n",
    "        circle = plt.Circle((x,y), radius=si, ec=ec, fc=fc, transform=ax.transData._b,\n",
    "                            zorder=zorder, lw=lw, fill=True)\n",
    "        ax.add_artist(circle)\n",
    "        \n",
    "\n",
    "def filter(sobol_indices, names, locs, criterion, threshold):\n",
    "    if criterion in ['ST', 'S1', 'S2']:\n",
    "        data = sobol_indices[criterion]\n",
    "        data = np.abs(data)\n",
    "        data = data.flatten() # flatten in case of S2\n",
    "        # TODO:: remove nans\n",
    "        \n",
    "        filtered = ([(name, locs[i]) for i, name in enumerate(names) if \n",
    "                     data[i]>threshold])\n",
    "        filtered_names, filtered_locs = zip(*filtered)\n",
    "    elif criterion in ['ST_conf', 'S1_conf', 'S2_conf']:\n",
    "        raise NotImplementedError\n",
    "    else:\n",
    "        raise ValueError('unknown value for criterion')\n",
    "\n",
    "    return filtered_names, filtered_locs\n",
    "\n",
    "\n",
    "def plot_sobol_indices(sobol_indices, criterion='ST', threshold=0.01):\n",
    "    '''plot sobol indices on a radial plot\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    sobol_indices : dict\n",
    "                    the return from SAlib\n",
    "    criterion : {'ST', 'S1', 'S2', 'ST_conf', 'S1_conf', 'S2_conf'}, optional\n",
    "    threshold : float\n",
    "                only visualize variables with criterion larger than cutoff\n",
    "             \n",
    "    '''\n",
    "    max_linewidth_s2 = 15#25*1.8\n",
    "    max_s_radius = 0.3\n",
    "    \n",
    "    # prepare data\n",
    "    # use the absolute values of all the indices\n",
    "    #sobol_indices = {key:np.abs(stats) for key, stats in sobol_indices.items()}\n",
    "    \n",
    "    # dataframe with ST and S1\n",
    "    sobol_stats = {key:sobol_indices[key] for key in ['ST', 'S1']}\n",
    "    sobol_stats = pd.DataFrame(sobol_stats, index=problem['names'])\n",
    "\n",
    "    smax = sobol_stats.max().max()\n",
    "    smin = sobol_stats.min().min()\n",
    "\n",
    "    # dataframe with s2\n",
    "    s2 = pd.DataFrame(sobol_indices['S2'], index=problem['names'], \n",
    "                      columns=problem['names'])\n",
    "    s2[s2<0.0]=0. #Set negative values to 0 (artifact from small sample sizes)\n",
    "    s2max = s2.max().max()\n",
    "    s2min = s2.min().min()\n",
    "\n",
    "    names = problem['names']\n",
    "    n = len(names)\n",
    "    ticklocs = np.linspace(0, 2*pi, n+1)\n",
    "    locs = ticklocs[0:-1]\n",
    "\n",
    "    filtered_names, filtered_locs = filter(sobol_indices, names, locs,\n",
    "                                           criterion, threshold)\n",
    "    \n",
    "    # setup figure\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111, polar=True)\n",
    "    ax.grid(False)\n",
    "    ax.spines['polar'].set_visible(False)\n",
    "    ax.set_xticks(ticklocs)\n",
    "\n",
    "    ax.set_xticklabels(names)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_ylim(ymax=1.4)\n",
    "    legend(ax)\n",
    "\n",
    "    # plot ST\n",
    "    plot_circles(ax, filtered_locs, filtered_names, max_s_radius, \n",
    "                 sobol_stats['ST'], smax, smin, 'w', 'k', 1, 9)\n",
    "\n",
    "    # plot S1\n",
    "    plot_circles(ax, filtered_locs, filtered_names, max_s_radius, \n",
    "                 sobol_stats['S1'], smax, smin, 'k', 'k', 1, 10)\n",
    "\n",
    "    # plot S2\n",
    "    for name1, name2 in itertools.combinations(zip(filtered_names, filtered_locs), 2):\n",
    "        name1, loc1 = name1\n",
    "        name2, loc2 = name2\n",
    "\n",
    "        weight = s2.loc[name1, name2]\n",
    "        lw = 0.5+max_linewidth_s2*normalize(weight, s2min, s2max)\n",
    "        ax.plot([loc1, loc2], [1,1], c='darkgray', lw=lw, zorder=1)\n",
    "\n",
    "    return fig\n",
    "\n",
    "\n",
    "from matplotlib.legend_handler import HandlerPatch\n",
    "class HandlerCircle(HandlerPatch):\n",
    "    def create_artists(self, legend, orig_handle,\n",
    "                       xdescent, ydescent, width, height, fontsize, trans):\n",
    "        center = 0.5 * width - 0.5 * xdescent, 0.5 * height - 0.5 * ydescent\n",
    "        p = plt.Circle(xy=center, radius=orig_handle.radius)\n",
    "        self.update_prop(p, orig_handle, legend)\n",
    "        p.set_transform(trans)\n",
    "        return [p]\n",
    "\n",
    "def legend(ax):\n",
    "    some_identifiers = [plt.Circle((0,0), radius=5, color='k', fill=False, lw=1),\n",
    "                        plt.Circle((0,0), radius=5, color='k', fill=True),\n",
    "                        plt.Line2D([0,0.5], [0,0.5], lw=8, color='darkgray')]\n",
    "    ax.legend(some_identifiers, ['ST', 'S1', 'S2'],\n",
    "              loc=(1,0.75), borderaxespad=0.1, mode='expand',\n",
    "              handler_map={plt.Circle: HandlerCircle()})\n",
    "\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "fig = plot_sobol_indices(Si, criterion='ST', threshold=0.005)\n",
    "fig.set_size_inches(7,7)\n",
    "#plt.savefig('JASSS figures/Figure 8 - Interactions.pdf', bbox_inches='tight')\n",
    "#plt.savefig('JASSS figures/Figure 8 - Interactions.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the sheep-gain-from-food variable has strong interactions with the wolf-gain-from-food and sheep-reproduce inputs in particular. The size of the ST and S1 circles correspond to the normalized variable importances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the kill_workspace() function shuts down the NetLogo instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "netlogo.kill_workspace()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
